WORLD WIDE WEB COVERAGE OF AGRICULTURAL
ISSUES: A CONTENT ANALYSIS
by
CLINT WALKER SAUNDERS, B.S.
A THESIS
IN
AGRICULTURAL EDUCATION
Submitted to the Graduate Faculty
of Texas Tech University in
Partial Fulfillment of
the Requirements for
the Degree of
MASTER OF SCIENCE
Approved
August, 2002
ACKNOWLEDGMENTS
Several people are responsible for a project of this nature to come
together. The members of my committee, as well as the entire department of
agricultural education and communications had a hand in my graduate career,
and I sincerely appreciate all the time and patience that those in the department
have provided me.
First of all, I would like to thank my chair, Dr. Akers. You definitely have
been someone I appreciate, respect, and have learned a great deal from. Your
knowledge and constant encouragement has helped me through the long
graduate school days.
Dr. Lockaby, thank you for serving as a committee member and for all of
your input throughout this project I appreciate everything you have done for me,
all the way back to my undergraduate years.
You and Dr. Akers have
undoubtedly made my whole college experience a great one.
Dr. Lawver, your presence as a committee member was invaluable as
well. Thank you for your willingness to lend a hand at all times, as well as your
guidance throughout my graduate career.
Dr. Baker, thank you for the opportunity to work with such a great group of
people. Your vast knowledge of research and your enthusiasm is greatly
appreciated. I feel honored to have been a part of your team in the department.
Mary Lou and Irita, thanks for everything. The two of you single-handedly
keep everyone in line. You both make being a graduate student a little easier,
and you are truly appreciated.
Thanks to Amanda Jo, Caitlyn, Kelly, John, and Vanessa for making the
office life a little better. Kelly and Vanessa, I will never be able to repay you for
serving as my panel of experts. Thank you for all that you have done in helping
me complete this study.
Thanks to the rest of the department for helping me out through my
graduate studies. Everyone has been willing to help at some time or another,
and I sincerely appreciate all of you.
Most of all, I would like to thank my family for their love and support
throughout my entire life. Mom and Dad, thanks for the years of encouragement.
Mom, you have provided me with the courage to reach my goals and you have
pushed me to succeed. Dad, thanks for instilling in me a strong agricultural
background. Thanks to Shawnda, T., Stacy, Me-me, and Big Lloyd for always
being there and always believing in me. And thanks to my nephew, Tucker, for
being an inspiration in my life.
Ill
TABLE OF CONTENTS
ACKNOWLEDGMENTS
ABSTRACT
vi
LIST OF TABLES
vi
LIST OF FIGURES
ix
CHAPTER
I. INTRODUCTION
1
Background
1
Statement of the Problem
7
Purpose and Objectives
8
Definition of Terms
8
Limitations to the Study
11
Assumptions
11
Significance of the Study
12
II. REVIEW OF LITERATURE
13
Literacy
13
Agricultural Literacy
14
Internet
21
News Analysis
22
News Analysis in Agriculture
25
Summary of Review of Literature
26
IV
III. METHODOLOGY
28
Design
28
Population and Sample
29
Instrumentation
30
Data Collection
34
Data Analysis
34
IV. RESULTS AND FINDINGS
36
Findings Related to Objective One
37
Findings Related to Objective Two
41
Findings Related to Objective Three
44
Findings Related to Objective Four
47
Findings Related to Objective Five
48
V. CONCLUSIONS AND RECOMMENDATIONS
55
Summary
55
Conclusions Related to Objective One
56
Conclusions Related to Objective Two
56
Conclusions Related to Objective Three
.57
Conclusions Related to Objective Four
59
Conclusions Related to Objective Five
60
Recommendations
61
REFERENCES
63
APPENDIX
A. SENTENCE BREAKDOWN
69
B. REPORT SENTENCES
80
C. INFERENCE SENTENCES
82
D. JUDGMENT SENTENCES
84
E. "OTHER" SENTENCES
87
VI
ABSTRACT
The purpose of this study was to evaluate the coverage of agriculture
available by popular agricultural websites on the World Wide Web for one
calendar month. The study sought to determine the level of bias in the identified
articles. Also, findings of this study were compared to two previous studies in
order to contrast the difference between reporters with an agricultural
background and those without an agricultural background.
The majority (55%) of these articles proved to be report sentences, which
are factual and verifiable sentences. Thirty-seven percent of the sentences were
judgment sentences, which are expressions of the writer's or quoted speaker's
opinions. Only 5% of the sentences were categorized as inference sentences,
which are subjective and immediately verifiable sentences.
Additionally, this study showed that reporters with an agricultural
background write more report sentences, much less inference sentences, and
slightly more judgment sentences.
Results of this study show the importance of agricultural literacy in the
media field in order to better report about the industry. More factual statements
by reporters will help provide a more accurate image of the agricultural industry.
VII
LIST OF TABLES
4.1
Number of AgOnline Articles
38
4.2
Number of AgDayta Articles
39
4.3
Number of AgWeb Articles
40
4.4
Number of Agricultural News Articles Selected from each Website....41
4.5
Concept Areas According to Frick et al
43
4.6
Sentence Types
44
4.7
Categories of Sentences
47
4.8
Judgment Sentences
48
4.9
Comparison of Number of Articles in 1997, 2000, and 2002
49
4.10
Comparison of Primary Concept Areas in 1997, 2000, and 2002
50
4.11
Comparison of Secondary Concept Areas in 1997, 2000, and 2002.. 51
4.12
Comparison of Sentence Types in 1997, 2000, and 2002
52
4.13
Comparison of Number of Sentences Occurring in each Sentence
Category in 1997, 2000, and 2002
53
4.14
Comparison of Number of Judgment Sentences in 1997, 2000, and
2002
54
A.1
Sentence Breakdown
70
VIII
LIST OF FIGURES
1.1
Possible Influences on Perceptions
4
1.2
Theory of Reasoned Action Model
6
2.3
Conceptual Framework for Agricultural Literacy
18
2.4
Hayakawa-Lowry Method
24
3.1
Percent of Articles from each Website
30
3.2
Hayakawa-Lowry Method
33
IX
CHAPTER I
INTRODUCTION
Background
Someway, somehow, agriculture affects everyone's life on an everyday
basis. However, Terry and Lawver (1995) stated that a substantial amount of
attention has been given to the fact that the American society is "agriculturally
ignorant." With each passing generation, this country has become one step
further removed from direct ties to production agriculture (Flood & Elliot. 1994).
Today's world is becoming more and more technologically advanced, and
agriculture is no exception. These changes, and many more, are propelling
agriculture to new levels. Because of these changes, and many more, the need
for agricultural literacy is becoming more important. According to the USDA
Agricultural Statistics Service (2001), the percent of the U.S. population involved
in production agriculture was 1.8% in the 1990s, compared to 16% in the 1950s.
Due to dramatic decreases in the farming and ranching population, it is
vital that the general public has accurate perceptions about agriculture, because
of its impact on our society, the economy, the environment, and personal health
(Terry & Lawver, 1995). Some of society's most controversial issues currently
involve agriculture, and the public's opinion of the food and fiber industry is highly
dependent on the agricultural literacy of those in the media. "Reporters must
strive to be neutral observers, collecting information and reporting it to let readers
form their own opinions" (Baker-Woods et al., 1997, p. 73). Writers should
1
present their stories by portraying both sides of the issue equally and excluding
their personal opinion of the subject (Sitton, 2000). Numerous studies have been
conducted investigating the role and impact of the press in delivering agricultural
news and information.
Journalists have a responsibility to report news both accurately and fairly.
If they fail in their duties, responsible reporting and consumption of
agricultural news will not occur. Likewise, important decisions affecting
the food and fiber industry may be made by misinformed individuals.
(Whitaker & Dyer, 1998, p. 445)
Journalists have many different means of disseminating information:
newspapers, television, radio, and the World Wide Web. According to the Office
of the U.S. Press Secretary (2000), almost one-half of all American households
now use the Internet, and more than 700 new households connect every hour.
The Internet definitely affects how people access information. Thomas
and Vistica (1998) conducted a poll as to where people receive most of their
news about current events. Their results are as follows: 6 1 % from television,
24% from newspapers, 8% from radio, 2% from the Internet or other online
services, and 1 % from magazines. Even though 2% seems fairly low, the figure
is expected to continue to rise. As Americans are becoming increasingly busier
in their everyday lives, the Internet provides them with information when they
want to receive the information. In fact, many newspapers, including national
and local, have gone on-line to publish their issues and accumulate information
for stories.
According to the U.S. Census Bureau (2000), 51% of Americans had a
computer in their household in the year 2000, and 41.5% had Internet access.
That is an increase of 15.3% from the 1998 statistic of American homes with
Internet access.
Determining the agricultural literacy rate of various groups has been the
topic of many researchers' studies. Studies of this nature include, but are not
limited to: elementary school students (Horn & Vinning, 1986; Mabie & Baker,
1996), high school students (Frick, Birkenholz, Gardner, & Machtmes, 1995),
university students (Flood & Elliot, 1994; Terry & Lawver, 1995), adults (Frick,
Birkenholz, & Machtmes, 1995), city and government leaders (Ryan & Lockaby,
1995), civic leaders (Bell & Lockaby, 1995), and 4-H members (Birkenholz, Frick,
& Machtmes, 1995). A greater part of these studies have indicated that society is
not literate in the subject of agriculture.
Further agricultural literacy studies include studies by Hess (1997) and
Hagins (2000). In 1997, Hess evaluated the Associated Press wire service
coverage of agricultural issues. Hess found there was more negative bias than
positive bias towards agriculture (Hess, 1997).
In 2000, Hagins again
researched the Associated Press wire service coverage of agricultural issues and
compared the findings to the study in 1997 by Hess. Hagins found that reporters
were writing more articles about agriculture, and there were slightly less
judgment sentences.
"The mass media is the primary source used by people to gather initial
awareness...mass media sources have a great influence upon public perception"
(Rogers, as cited in Terry, Dunsford, Lacewell, & Gray, 1996, p. 215). Schoell
and Guiltinan (1995) wrote, "perception is the process through which an
individual selects relevant stimuli (information) from the environment, organizes
them, and assigns meaning to them" (p. 145). Bell and Lockaby (1995) stated,
"people's backgrounds appear to influence their opinion and knowledge about
agriculture" (p. 7). There are an abundance of different factors that can influence
a person's perception of agriculture and other issues. Figure 1.1 attempts to
show the outside factors that can affect an individual's perceptions. Where
people came from, what type of environment they were raised in, and the
occupation of their parents can all affect their perceptions.
Print
Media
Family
Formal
Knowledge
_J
\
Peers
^ \
i
/
C _ p«3rception ^
Direct
Experience
^V
Basic
Knowledge
Geographic
Location
^ < -
\
\
Television
Cultural
Background
Figure 1.1: Possible Influences on Perceptions, (Hagins, 2000).
A simplified version of the Theory of Reasoned Action is shown in Figure
1.2. Since 1967, researchers have utilized this theory to explain and predict a
variety of human behaviors. Based on the premise that humans are rational and
that the behaviors being explored are under volitional control, the theory provides
a construct that links individual beliefs, attitudes, and behavior (Fishbein,
Middlestadt, & Hitchcock. 1994). This theory focuses on the translation of beliefs
about behavior and perceptions regarding behavior change. It also incorporates
the social and interacting aspects of human behavior. Included in the Theory of
Reasoned Action is the influence of others.
The theory of reasoned action represents a comprehensive integration of
attitude components into a structure that is designed to lead to both better
explanation and better predictions of behavior. The theory of reasoned action
model incorporates a cognitive component, an affective component, and a
conative component. The cognitive component represents a person's knowledge
and perceptions that are acquired by a combination of direct experience and
related information from various sources, such as the mass media. The affective
component refers to a person's feelings or emotions about a particular issue or
object. The conative component is concerned with the likelihood or tendency
that an individual will undertake a specific action or behave in a particular way
(Schiffman & Kanuk, 1997).
Beliefs that the
behavior leads to
certain outcomes
1
Attitude toward
the behavior
Evaluation
of the outcomes
Intenfion
Behavior
Beliefs that specific referents
think I should or should not
perform the behavior
Subjective norm
Motivation to
comply with the
specific referents
Figure 1.2: Theory of Reasoned Action Model (simplified version). Source:
Adapted from Ajzen and Fishbein (1980, p. 84).
The theory of reasoned action depicts the process a person goes through
to reach a desired outcome or behavior. This process is extremely important to
those studying the perceptions of agriculture. The theory of reasoned action will
help to form a person's attitude or perception, which in turn leads to a specific
behavior or no behavior at all.
Statement of the Problem
American farmers are becoming more and more efficient in food
production practices as technology advances. Thus, members of our society
involved in the production of our food and fiber have decreased (Birkenholz,
Harris, & Pry, 1994). With less and less agricultural workers, the agricultural
literacy level of our nation suffers. According to the U.S. Census Bureau (2000),
the Internet has become a major way of disseminating news. Almost 1 in 5
adults use the Internet at home to check on news, weather, or sports (U.S.
Census Bureau, 2000). Due to the fact that the use of the Internet to receive
news information is rising, researchers should study coverage of agricultural
issues on the World Wide Web to evaluate agricultural literacy. Additionally,
Hagins (2000) conducted a similar study evaluating agricultural stories on the
Associated Press and recommended that a study be done to evaluate the
coverage of agricultural news articles from agricultural publications. This study
sought to evaluate such coverage.
Purpose and Ohjftr.tivpg
The purpose of this study was to evaluate the coverage of agriculture
available by popular agricultural websites on the World Wide Web for one
calendar month. The following objectives were formulated to accomplish the
purpose of this study:
1. To identify all the articles written about agriculture on the most popular
agricultural websites on the World Wide Web for a selected month;
2. To categorize World Wide Web articles into agricultural literacy
concept areas;
3. To categorize the sentences in the identified articles using the
Hayakawa-Lowry News Bias Categories;
4. To determine bias of judgment statements in the identified articles; and
5. To compare findings from agricultural publications to that of nonagricultural publications (Hess, 1997; Hagins, 2000).
Definition of Terms
For the purpose of this study, the following terms were defined.
Agricultural Literacy—The understanding and knowledge necessary to
synthesize, analyze, and communicate basic information about agriculture
(Campbell, 1997).
Associated Press (AP) Aoricultural News Stories—Stories that involve
agriculture, collected from the Associated Press wire service (Hagins, 2000).
8
Bias—To be or to show prejudice (Woolf, 1974).
Content Analysis—A research design technique for the objective,
systematic, and quantitative description of the manifest content of communication
(Berelson, 1952).
Internet—A series of linked, world-wide computer networks (Campbell,
1997).
News Article—Artidfi that tells a story for the purpose of informing.
World Wide Web (WWW)—A part of the Internet. A hypertext system
which connects any number of documents and computer servers (Campbell,
1997).
World Wide Web (WWW) Agricultural News Stories—Stories that involve
agriculture on the World Wide Web.
Report Sentences—Factual and verifiable sentences (Hayakawa, 1972).
Inference Sentences—Subjective and immediately verifiable sentences
(Hayakawa, 1972).
Judgment Sentences—Expressions of the writer's or quoted speaker's
opinions (Hayakawa, 1972).
Reported Attributed Sentences—Information which is factual and
attributed to the source (Lowry, 1971).
Report Unattributed Sentences—Information which is factual without citing
someone as the source (Lowry, 1971).
Inference Labeled Sentences—Statftmfint.Q about the unknown based on
the known. These are often interpretations or generalizations of events. Labeled
inferences use "tip-ofT" specific words such as appear, could, may, perhaps,
possible...to let the reader know the information is subjective to some extent
(Lowry, 1971).
Inference Unlabeled Sentences—Statements about the unknown based
on the known. Often interpretations or generalizations of events, without "tip-ofl"
words. Considered to have more bias because the "tip-off' is not used to "warn"
the reader (Lowry, 1971).
Judgment Attributed. Favorable Sentences—Statements of the writer's
approval or disapproval of an event, person, object, or situation that are
attributed to a source and favorable toward the subject (Lowry, 1971).
Judgment Attributed. Unfavorable Sentences—Statements of the writer's
approval or disapproval of an event, person, object, or situation that are
attributed to a source and unfavorable toward the subject (Lowry, 1971).
Judgment Unattributed. Favorable Sentences—Statements of the writer's
approval or disapproval of an event, person, object, or situation that are not
attributed to a source, but are favorable toward the subject (Lowry, 1971).
Judgment Unattributed. Unfavorable Sentences—Statements of the
writer's approval or disapproval of an event, person, object, or situation that are
not attributed to the source, but are unfavorable to the subject (Lowry, 1971).
10
other Sentences—All other sentences. These sentences normally include
rhetorical questions, and introductory statements (Lowry, 1971).
Limitations to the Studv
Limitations of this study must be taken into consideration in the application
of the results. The following limitations of the study should be considered:
1. This study was limited to one month of a calendar year. Therefore, it is
possible that different results could be found during another month of
the year or during a different agricultural season.
2. This study was limited to the current availability of information the
public receives about agriculture from the World Wide Web.
3. Communication experts from the agriculture sector that were trained in
the Hayakawa-Lowry News Bias categories were used to categorize
sentences. It is possible that different results could be found if experts
from other areas were used.
Assumptions
The following assumptions were considered when researching this study:
1. These findings will indicate the level of agricultural literacy of the media
personnel, who wrote these World Wide Web news stories.
2. The articles from the selected websites are written by reporters with an
agricultural background.
11
Significance of Studv
The American public's knowledge of agriculture is extremely important.
The success or failure of the agricultural industry can be directly related to the
general public's view of agriculture. The general public's view of agriculture is
usually directly related to what is reported by the media. This study evaluated
the agricultural coverage by media on the World Wide Web. Additionally, this
study will assist professionals in the agricultural industry in deciding how to
educate the media to report factual statements about agriculture.
12
CHAPTER II
REVIEW OF LITERATURE
This review of related literature was developed to establish a theoretical
framework and background for the study. Research has been reviewed in order
to support the objectives of the study. A basic understanding of literacy was
necessary in order to further understand agricultural literacy and its importance to
our society.
This review consists of literature from the following topics: literacy,
agricultural literacy, Internet, news analysis, and news analysis in agriculture.
Literature reviewed included theses, papers from conference presentations,
articles from professional journals, magazines, books, and other sources.
Literacy
"A literate person is one who is able to read and write or is well-educated;
having or showing extensive knowledge, learning, or culture" (Nuefeldt &
Guralnik, 1988, p. 789). Literacy basically describes the ability to read and write.
It is a battle that is waged in the United States and is a problem in such faraway
places as New Zealand (Long, 1994).
The value of literacy is important because, "people make sense of literacy
as a social phenomenon... literacy lies at the root of their attitudes... and their
actions" (Barton, 1990, p. 7).
13
Determining what is literate and what is not differs from person to person.
Various authors have offered many different definitions. Bornmuth (1975)
provided a definition of literacy as "... the ability to respond competently to realworld reading tasks" (p. 65). Miller (1989) defined^basic literacy as including the
capacity to read packages, traffic signs, and a bus schedule. Rush, Moe, and
Storlie (1986) define occupational literacy as being able to read required workrelated materials. According to Frick, Birkenholtz, and Machtmes (1995), the
degree of literacy is a measure without absolute standards. All these different
perspectives help categorize the basic concept of literacy.
Literacy has gone beyond these meanings and has taken on new
definitions through the years. Literacy has been expanded to fields outside of
just reading and writing: computer literacy, math literacy, cultural literacy,
bilingual literacy, and agricultural literacy (Ahmadi & Helms, 1994).
Agricultural Literacy
Mawby (1985) stated "few issues are of greater importance to the world
than adequate food supplies, proper food use, and knowledge about the
components of the agricultural industry" (p. 7). Agriculturists have an important
job, and that is to feed and clothe our nation. Due to the success of the
American farmer, most citizens are not required to work in production agriculture
(Birkenholz, 1990). As a result, the general public is becoming increasingly
unaware of the source and methods used in the production of their food (National
14
Research Council, 1998; Raven, 1994). This problem can be identified as a lack
of "agricultural literacy" (Russell, McCracken, & Miller. 1990; Frick, Kahler. &
Miller, 1992).
By using the Delphi technique, Frick, Kahler, and Miller (1992) developed
the following broad definition of agricultural literacy:
Agricultural literacy can be defined as possessing knowledge and
understanding of our food and fiber system. An individual possessing
such knowledge would be able to synthesize, analyze, and communicate
basic information about agriculture. Basic agricultural information
includes: the production of plant and animal products, the economic
impact of agriculture, its social significance, agriculture's important
relationship with natural resources and the environment, the marketing of
agricultural products, public agricultural policies, the global significance of
agriculture, and the distribution of agricultural products, (cited in Ryan,
1995, p.10)
Agricultural literacy has also been defined as the goal of education about
agriculture (Williams, 1991).
Basically, the term agricultural literacy means to
educate the public about agriculture. Leising (1994) described agricultural
literacy as possessing the knowledge and understanding of our food and fiber
system. A basic knowledge of agriculture is especially important where it is the
major industry in a state and the lack of agricultural knowledge and experience
impedes economic development (Williams, 1991).
As fewer members of our society become involved in production
agriculture, the need for agricultural literacy becomes more imperative. Law
(1990) pointed out the need for agricultural literacy when he stated:
Americans know very little about the social and economic relevance in the
United States, and agriculture is too important a subject to be taught only
to a relatively small proportion of students enrolled in vocational
15
agriculture. As special interest groups revolving around issues such as
animal rights, pesticide usage, soil and water conservation, and other
environmental concerns gain more media and public attention, it becomes
even more important that the general public have some background and
understanding of not only what agriculture is all about, but on how it
affects each person's life on a daily basis, (p. 5)
Generations of farmers have understood all aspects of raising crops,
livestock, and blending those with available resources (Frick & Spotanski, 1990).
According to the USDA Agricultural Statistics Service (2001), only 1.8% of the
U.S. population was involved in production agriculture in the 1990's. Raven
(1994) spoke of the importance of agricultural literacy:
For the first time in our history, a vast majority of the population is more
than one generation away from production agriculture. No longer do
children have a grandparent or close relative who is a farmer or rancher.
As a result, most Americans know little about food and fiber production, its
social and economic significance in the United States and its links to
human health and environmental quality, (p. 37)
Understanding basic agricultural terms is another approach to agricultural
literacy. If people, including reporters, cannot comprehend basic terms like
tillage, pesticides, fertilizer, growth hormones, cultivation, or soil erosion, they
can hardly be expected to follow discussion of agricultural issues in the political
arena or in the media (Frick & Spotanski, 1990).
Several researchers have discovered that the diffusion of agricultural
information has occurred for some time now. According to Parmley, May, and
Hutchinson (1996):
Agricultural literacy is not a new concept. The development of a
fundamental understanding of food production had been a feature of
informal and formal education efforts until the early years of the twentieth
century. A review of informal and formal education practices from earlier
16
times should provide some significant concepts to consider as we
continue the agricultural literacy agenda, (p. 21)
The National Academy of Sciences' Committee on Agricultural Education
(1998) states that by achieving the goal of agricultural literacy, informed citizens
are able to participate in establishing the policies that will support a competitive
agricultural industry in this country and abroad will be produced. According to
Pope (1990), "the real need for an agriculturally literate society is knowledge of
the impact the industry, as a whole, has upon our daily lives" (p. 23). Frick and
Elliot (1995) created a conceptual framework in order to better comprehend the
factors that contribute to knowledge and perceptions about agriculture. Two
components that are integral to one's agricultural literacy are knowledge base
and perceptions, which are measured and assessed in their study (Frick & Elliot,
1995). Figure 2.3 shows their framework. It includes three factors: personal
characteristics, education, and participation in agricultural activities. All three of
these factors illustrate the underlying forces that contribute to the formation of
one's knowledge base and perceptions.
17
Figure 2.3: Conceptual Framework for Agricultural Literacy. Source: Frick and
Elliot, 1995.
As for the general public, knowledge of agriculture (or the lack thereof)
can influence decisions and opinions concerning agricultural policy (Hays, 1993).
Consequently, all United States citizens should have a minimum level of
understanding of agriculture and its impact to make decisions about policies and
issues affecting agriculture (Terry et al., 1996; Russell, McCracken & Miller,
1990).
Agricultural literacy studies have been conducted through the years with
different goals in mind.
Pals and Waitley (1996) created a project to assist Idaho elementary
school students to learn more about agriculture. With the help of this project, the
students made an improvement in their writing skills and developed a program to
18
be presented to fourth graders. The Idaho Agriculture in the Classroom (AITC)
program provided the curriculum for this project. Results indicated a sufficient
amount of agricultural literacy available to the teachers; however, there were not
enough instructors to teach the importance of the subject.
Moore and Violet (1996) studied agricultural education in two Montana
middle schools located in the southwestern part of the state. One of the schools
was located in an isolated area of Montana where agriculture was the leading
industry, and had 77 students enrolled from grades 7-12. The second school,
located in a suburban area where the main source of income was coal mining,
had 700 students from grades 6-12. The study showed a significant amount of
agricultural literacy material available; however, teachers were unable to
determine the area of agriculture in which to focus their instruction.
A study evaluating agricultural awareness among eleventh grade students
in small, rural Missouri schools, with some containing a secondary agricultural
program and some not, was performed by Wright, Stewart, and Birkenholz
(1994). According to the results of this study, students that were enrolled in an
agricultural program were more literate and had a more positive perception of
agriculture, as compared to those students not enrolled in an agricultural
program.
Frick, Birkenholz, and Machtmes (1995) completed a study looking at
agricultural awareness of 4-H groups. Overall, results showed the knowledge
level of 4-H members was high, but varied.
19
Another study, conducted by Terry and Lawver (1995), sought to evaluate
university students' perceptions of agriculturally related issues. The population
consisted of 400 Texas Tech University students. Results revealed students
believed agriculture had an overall positive impact on the economy and
environment, and male students were more positive on issues such as food
safety and animal welfare, as compared to female students.
Adult agricultural literacy has also been studied, though not in the amount
of other age groups. Terry (1994) sought to evaluate the awareness and
perceptions of agriculture of television reporters in Texas. Terry's study revealed
that the majority of television reporters had little personal or professional contact
with agriculture. Reporters also did not have the background characteristics or
educational and organizational experiences normally associated with
agriculturally literate people. The study also revealed that while most reporters
not only enjoy reporting on agricultural news, they also feel qualified to do so,
however, few possess the technical knowledge and appropriate understanding
about agriculture to accurately inform the public about the industry
A national study on agricultural literacy found that, in general, most
Americans have little knowledge about agriculture and its social and economic
significance in the United States, and particularly, its links to human health and
environmental quality (National Academy of Science. 1988).
20
Internet
The Internet began as a means of decentralizing information sources for
the military (Pool, Blanchard, & Hale, 1995). The United States Department of
Defense in 1969. established ARPAnet, a web that linked universities,
government labs, and key defense industries. In 1987, the National Science
Foundation combined ARPAnet with its larger NSFNet Today, the Internet is a
combination of NSFNet, ARPAnet, and about 10.000 other networks (Pool,
Blanchard. & Hale. 1995).
The Internet is used for a variety of private, commercial, and international
purposes, including: teaching, research, entertainment, and the gaining of
information (Fleck, 1994). The World Wide Web began as a port in order to
access the many Internet resources. The web is "an information source that links
documents across the Internet" (Lambert & Howe. 1993, p. 17).
The Internet is increasingly growing as a vital transmitter of information.
Approximately one million new subscribers 'log on' to the Internet every month
(Miller, 1996).
Web sites offer news, commentary, and other media outlets. The Internet
is growing in popularity as the medium of choice. Connecting to the Internet has
become a daily occurrence.
Good journalism relies on news that is timely, interesting, and accurate
(Reddick & King, 1995). The Internet is providing journalists a pathway to
21
accomplish their goals. Online information allows reporters more information in a
faster manner than any other source available (Reddick & King, 1995).
"The dissemination of agricultural information has always been crucial to
individuals involved in production agriculture" (Burns, 1998, p. 12). According to
the National Agricultural Statistics Service (2001). 55% of farms in the United
States had access to a computer. 29% of farmers and ranchers used their
computer for farm business, and 43% had Internet access.
In 2001. AgWeb conducted market research by surveying several hundred
randomly selected farmers and ranchers regarding their Internet usage (M.
Gibson, personal communication. December 4. 2001). They found the most
accessed news sites included AgWeb.com. AgDayta.com. and Agriculture.com.
Therefore, those three web sites were used for the purpose of this study.
News Analysis
S.I. Hayakawa developed a method of analyzing sentences in news
articles by assigning each sentence to one of three categories: (a) report
sentences, (b) inference sentences, and (c) judgment sentences.
sentences are factual and verifiable.
Report
Inference sentences are defined as
statements about the unknown based on the known. Statements of the writer's
approval or disapproval are referred to as judgment sentences. This system was
created in order to determine if reporters were being truthful or factual when
22
writing their news articles. By using this method, researchers can determine
when reporters have provided their opinion in the news articles.
At times, it could be impossible to deliver a report without being impartial
towards the subject matter, admits Hayakawa. Additionally, he warns the public
to be "aware of the favorable and unfavorable feelings that certain words and
factors can arouse" (p. 43). This is defined as slanting, according to Hayakawa.
"Slanting gives no explicit judgments, but differs from reporting in that it
deliberately makes certain judgments inescapable" (p. 43).
Lowry (1971) used Hayakawa's method to conduct a content analysis of
network television news. Later, Lowry (1985) updated the original instrument
created by Hayakawa. He conducted research to establish the construct validity
of the Hayakawa-Lowry news bias categories. He found that:
Hayakawa's distinctions between reports, inferences, and judgments are
indeed perceived by untrained audience members and actually do affect
their perceptions of news objectivity and that the differences measured by
these categories when used by researchers in content analysis studies
are differences that do indeed make a meaningful difference to
consumers, (p. 759)
Additionally, Lowry found that "negative judgments are sometimes perceived as
more biased than are positive judgments" (p. 579).
As compared to the original, three-category method by Hayakawa, the
updated Hayakawa-Lowry method is used to define sentences and place each
into one of nine categories.
The categories include: (1) report attributed
sentences, (2) report unattributed sentences, (3) inference labled sentences, (4)
inference unlabled sentences, (5) judgment attributed (favorable sentences), (6)
23
judgment attributed (unfavorable sentences). (7) judgment unattributed
(favorable sentences). (8) judgment unattributed (unfavorable sentences), and
(9) other sentences. By using this method, researchers are able to determine if
media is reporting facts, opinions, or a combination of the two. Figure 2.4 shows
the breakdown of the Hayakawa-Lowry method.
Sentence
Judgment
Inference
Report
i
( Other )
I
^Attributed J T Unattributed)
i
1
C Labeled ")
(^Unlabeled J
i.
Unattributed
Attributed
£
(
I
Favorable J ( Unfavorable J ( Favorable J ( Unfavorable )
Figure 2.4: Hayakawa-Lowry Method. Source: Unpublished paper by Lockaby.
Akers. Kieth, and Hagins, 2002.
24
News Analysis in Agriculture
Researchers have used the Hayakawa-Lowry method to evaluate
agricultural coverage. Terry et al. (1996) found a contributing problem of
agricultural literacy was the lack of agricultural coverage in national news
periodicals. Researchers used the Hayakawa-Lowry method to rate the level of
bias in the chosen articles. Each article was categorized into primary and
secondary concept areas: (1) societal and global significance of agriculture. (2)
public policy in agriculture, (3) agriculture's relationship with the environment and
natural resources, (4) plant science, (5) animal science, (6) processing of
agricultural products, and (7) marketing and distribution of agricultural products.
Discovered results revealed that there was a lack of agricultural coverage by
three nationally known magazines, which in turn, contributes to the lack of
agricultural literacy in society.
Researchers recommend others using the
Hayakawa-Lowry News Bias Categories method in future research on publication
bias.
Sitton (2000) conducted a study evaluating the news published during the
year of 1998 about swine concentrated feeding operations by the two largest
Oklahoma newspapers, as well as profile the people that wrote the news. Sitton
found the majority of the sentences were report sentences, and the majority of
the judgment sentences were negative towards agriculture. When reporters
were profiled, Sitton found the vast majority to be males, as well as possessing
no agricultural background.
25
Hess (1997) performed a study to evaluate the coverage of agriculture
through the Associated Press (AP) wire service. Hess found there was adequate
coverage of agricultural topics, however, many reporters were using personal
opinions when writing instead of factual statements. The study also concluded
there was more negative bias than positive bias toward agriculture.
Hagins (2000) replicated Hess's (1997) study in order to evaluate the
current coverage of agriculture available through the Associated Press wire
service, and to compare the findings to Hess's (1997) findings. Hagins found
that reporters are writing more agricultural stories, and that there is sufficient
coverage of agricultural topics on the Associated Press wire service. The study
also found that reporters were writing slightly less judgment sentences.
Summan/ of Review of Literature
In this review of literature, the need for additional agricultural literacy was
established and is evident by using research on literacy, agricultural literacy,
Internet, and news analysis.
After reviewing related literature, it is evident that literacy is not only a
problem locally, but worldwide as well. Determining whether a person is literate
or not differs from situation to situation. Many different views on what is literate
or illiterate exist. Being able to understand and read basic, everyday tasks seem
to be the most common definition of literacy.
26
Agricultural literacy has been a topic that has been studied for some time.
Researchers have been evaluating the levels of agricultural literacy of different
age groups. Studies in this area show an overall low agricultural literacy level of
society. Research results show that reporters present agricultural issues in a
negative manner more so than in a positive manner.
The Internet seems as though it came about overnight. It exploded into
households, and is now considered a tool used on a daily basis for the gathering
of information.
As for news analysis, Hayakawa and Lowry developed a method to
analyze news articles in order to determine reporter biases.
Different
researchers such as Terry et al. (1996). Hess (1997), Peper-Sitton (1989), and
Hagins (2000) have used this method to acquire the results of their study in
relation to agriculturally related news. Researchers recommended it for use in
future research of this matter.
27
CHAPTER III
METHODOLOGY
The purpose of this study was to evaluate the coverage of agriculture
available by popular agriculture websites on the World Wide Web for one
calendar month. The following objectives were formulated to accomplish the
purpose of this study:
1. To identify all the articles written about agriculture on the most popular
agricultural websites on the World Wide Web for a selected month;
2. To categorize World Wide Web articles into agricultural literacy
concept areas;
3. To categorize the sentences in the identified articles using the
Hayakawa-Lowry News Bias Categories;
4. To determine bias of judgment statements in the identified articles; and
5. To compare findings from agricultural publications to that of nonagricultural publicafions (Hess, 1997; Hagins, 2000).
Design
A descriptive research design was used for this study. Ary, Jacobs, and
Razavieh (1996) state that descriptive research asks questions concerning the
nature, incidence, or distribution of educational variables and relationships
among these variables. This study sought to evaluate agricultural articles taken
28
from popular agricultural websites; thus, a descriptive design was deemed the
most appropriate.
Population and Sample
In 2001. AgWeb conducted market research by surveying several hundred
randomly selected farmers and ranchers regarding their Internet use (M. Gibson,
personal communication. December 4, 2001). They found the most accessed
news sites included AgWeb.com, AgDayta.com, and Agriculture.com. Therefore,
those three websites were used for the purpose of this study.
All articles, market reports, weather reports, etc. posted under the news
section of each of the three websites were downloaded for January 2002, totaling
1,132 items.
News was gathered five times per week. Results from this
particular month should not be inferred to other months of the year.
A panel of three then sorted through the items and selected news articles,
or articles that tell a story for the purpose of informing. All market reports,
weather reports, links, and other items that did not fit the definition of news article
were deleted from the population.
The population of the study consisted of all news articles retrieved from
the three chosen websites for January 2002 (N=821). A systematic random
sample (n=262) was selected (Krejcie & Morgan, 1970) according to the
population size. AgWeb (n=152) posted the largest number of agricultural news
stories for the month of January 2002, representing 58% of the sample size.
29
AgOnline (n=61) had the second most agricultural news stories, with 23% of the
sample size. AgDayta (n=49) represented 19% of the sample size.
Figure 3.1 shows the percent of articles randomly selected from each
website.
Figure 3.1: Percent of Articles from each Website
Instrumentation
To conduct this study, a content analysis based on the Hayakawa-Lowry
news bias categories was used.
S.I. Hayakawa (1940) developed a system to analyze sentences in news
articles.
He placed the sentences into one of three categories: (a) report
sentences, (b) inference sentences, and (c) judgment sentences.
Report sentences are considered to be unbiased, and therefore, factual
and verifiable. When a reporter makes an inference, the accuracy of the
30
statement is weakened.
These statements can be subjective and not
immediately verifiable. Judgment sentences are expressions of the writer's or
quoted source's personal opinions. These sentences are considered biased by
the reporter or the reporter's source.
Lowry (1971) expanded Hayakawa's method, which includes six new
sentence categories, making a total of nine categories for the Hayakawa-Lowry
method. Lowry took into consideration attribution of the informafion and reporter
bias. The nine categories include:
Reported Attributed Sentences—Information which is factual and
attributed to the source (Lowry, 1971).
Report Unattributed Sentences—Information which is factual without citing
someone as the source (Lowry, 1971).
Inference Labeled Sentences—Statements about the unknown based on
the known. These are often interpretations or generalizations of events. Labeled
inferences use "tip-ofT specific words such as appear, could, may, perhaps,
possible...to let the reader know the information is subjective to some extent
(Lowry, 1971).
Inference Unlabeled Sentences—Statements about the unknown based
on the known. Often interpretations or generalizations of events, without "tip-off'
words. Considered to have more bias because the "tip-ofT is not used to "warn"
the reader (Lowry, 1971).
31
Judgment Attributed. Favorable Sentences—Statements of the writer's
approval or disapproval of an event, person, object, or situation that are
attributed to a source and favorable toward the subject (Lowry, 1971).
Judgment Attributed. Unfavorable Sentences—Statements of the writer's
approval or disapproval of an event, person, object, or situation that are
attributed to a source and unfavorable toward the subject (Lowry, 1971).
Judgment Unattributed. Favorable Sentences—Statements of the writer's
approval or disapproval of an event, person, object, or situation that are not
attributed to a source, but are favorable toward the subject (Lowry, 1971).
Judgment Unattributed. Unfavorable Sentences—Statements of the
writer's approval or disapproval of an event, person, objective, or situation that
are not attributed to the source, and unfavorable to the subject (Lowry, 1971).
Other Sentences—All other sentences. These sentences normally include
rhetorical questions and introductory statements (Lowry. 1971).
Lowry used a two-part study at Liberty University and Ohio University to
successfully establish the construct validity of the Hayakawa-Lowry News Bias
Categories. Lowry (1985) stated:
The assumptions underlying the Hayakawa-Lowry category system were
twice put to the test, and a group of subjects...for the most part, evaluated
the news stories and sentences as predicted. Thus, the results strongly
suggest that the differences measured by these categories, when used by
researchers in content analysis studies, are differences that do indeed
make a meaningful difference to news consumers, (p. 580)
32
Lowry dealt with problems of inter-rater reliability through the development of a
tested rater manual (Terry et al., 1996). Figure 3.2 shows the breakdown of the
Hayakawa-Lowry method.
Sentence
Report
Inference
Judgment
( Other )
r-mPm
(Attributed
) (Unattributed J
(Labeled)
(Unlabeled J
1
Attributed
( Favorable J
Unattributed
( Unfavorable J
( Favorable J ( Unfavorable j
Figure 3.2: Hayakawa-Lowry Method. Source: Unpublished paper by Lockaby.
Akers, Kieth, and Hagins, 2002.
33
Data Collection
Data collection began the first week of January 2002, using AgWeb.com,
AgDayta.com. and Agriculture.com. News was collected five times per week for
the entire month.
Data Analysis
A panel of three agricultural education and communications experts were
used to code the identified articles to ensure coder reliability. The experts were
trained in the Hayakawa News Bias Categories. Each sentence of the identified
articles was coded using the Hayakawa-Lowry News Bias Categories:
1. Report sentence/attributed,
2. Report sentence/unattributed,
3. Inference sentence/labeled,
4. Inference sentence/unlabeled,
5. Judgment sentence/attributed/favorable,
6. Judgment sentence/attributed/unfavorable,
7. Judgment sentence/unattributed/favorable,
8. Judgment sentence/unattributed/unfavorable,
9. All other sentences (Lowry, 1985).
The researcher and each expert coded all identified articles. All coding was
compared. Experts reviewed discrepancies unfil a consensus was reached on
the code assigned to each sentence.
34
The agricultural literacy concept areas developed by Frick, Birkenholz,
Gardner, and Machtmes (1995), were used to categorize the articles into
separate groups. The categories were: (1) Societal and Global Significance of
Agriculture; (2) Public Policy in Agriculture; (3) Agriculture's Relafionship with the
Environment and Natural Resources; (4) Plant Science; (5) Animal Science; (6)
Processing of Agricultural Products; and, (7) Marketing and Distributing of
Agricultural Products.
Descriptive statistics were used. Statistical analysis was performed using
Microsoft® Excel.
35
CHAPTER IV
RESULTS AND FINDINGS
The purpose of this study was to evaluate the coverage of agriculture
available by popular agriculture websites on the World Wide Web for one
calendar month. The following objectives were formulated to accomplish the
purpose of this study:
1. To identify all the articles written about agriculture on the most popular
agricultural websites on the World Wide Web for a selected month;
2. To categorize World Wide Web articles into agricultural literacy
concept areas;
3. To categorize the sentences in the identified articles using the
Hayakawa-Lowry News Bias Categories;
4. To determine bias of judgment statements in the identified articles; and
5. To compare findings from agricultural publications to that of nonagricultural publications (Hess, 1997; Hagins, 2000).
The results from each objective of this study are shown in this chapter.
The results are explained with the information and data found from the research
conducted.
36
Findings Related to Obiective One
Objective one was to identify all the articles written about agriculture on
the most popular agricultural websites on the World Wide Web for a selected
month. Agricultural news articles were collected during the month of January
2002, for a total of 821 articles. The average number of agricultural news stories
posted daily was 35.7. The number of articles varied daily.
The following three tables indicate the amount of agricultural news articles
posted each day during the selected month of January 2002, for each of the
three selected websites. Included in the tables are the articles included in the
entire population. Table 4.1 indicates the number of agricultural news articles
posted on AgOnline (www.agriculture.com), Table 4.2 indicates the number of
articles posted on AgDayta (www.agdayta.com), while Table 4.3 indicates the
number of articles posted on AgWeb (www.agweb.com). AgWeb posted the
highest number of agricultural news articles (434), AgDayta posted the second
highest (222), and AgOnline posted the least (165).
37
Table 4.1: Number of AgOnline Articles
Date
Number of Articles
January 1
0
January 2
4
January 3
4
January 4
3
January 7
11
January 8
8
January 9
10
January 10
9
January 11
10
January 14
7
January 15
6
January 16
8
January 17
8
January 18
7
January 21
8
January 22
8
January 23
10
January 24
9
January 25
7
January 28
9
January 29
9
January 30
7
January 31
3
Daily Average
7.17
TOTAL
165
38
Table 4.2: Number of AgDayta Articles
Date
Number of Articles
January 1
0
January 2
12
January 3
10
January 4
8
January 7
10
January 8
10
January 9
10
January 10
11
January 11
10
January 14
11
January 15
10
January 16
8
January 17
11
January 18
11
January 21
11
January 22
10
January 23
12
January 24
10
January 25
12
January 28
7
January 29
6
January 30
11
January 31
11
Daily Average
9.65
TOTAL
"222"
39
Table 4.3: Number of AgWeb Articles
Date
Number of Articles
January 1
0
January 2
13
January 3
17
January 4
21
January 7
23
January 8
21
January 9
22
January 10
19
January 11
29
January 14
23
January 15
20
January 16
19
January 17
22
January 18
21
January 21
6
January 22
27
January 23
21
January 24
13
January 25
15
January 28
21
January 29
13
January 30
24
January 31
24
Daily Average
18.87
TOTAL
434
40
The sample size, which was determined by a systematic random sampling
procedure, used for this study was 262. AgWeb (n=152) posted the largest
number of agricultural news stories for the month of January 2002. representing
58% of the sample size. AgOnline (n=61) had the second most agricultural news
stories, with 23% of the sample size. AgDayta (n=49) represented 19% of the
sample size.
Table 4.4 indicates the amount of agricultural news stories that were
randomly selected from each of the three websites. The total number of
sentences is also included in the table.
The total number of sentences in the selected articles was 3,360. The
average number of sentences per article was 12.82.
Table 4.4: Number of Agricultural News Articles Selected from each Website
Website
Number of Articles
%
Number of Sentences
AgOnline
61
23
497
AgDayta
49
19
545
AgWeb
152
58
2,318
262
100
3,360
TOTAL
Findings Related to Obiective Two
Objective two sought to categorize the agricultural news stories into
agricultural literacy concept areas, as primary and secondary categories. Each
article was analyzed by the panel of experts and placed into a primary and
41
secondary category, based upon the theme of the article. Frick, Birkenholz.
Gardner, and Machtmes (1995). developed the categories used to discriminate
between the concept areas. The seven categories include the following: societal
and global significance of agriculture, animal science, plant science, agriculture's
relationship with the environment and natural resources, public policy in
agriculture, marketing and distributing of agricultural products, and processing of
agricultural products.
The experts reviewed each article individually and determined the major
theme of the article. Once the main theme was determined, the experts then
came to consensus on a secondary theme of the story. For example, an article's
main topic could be cattle related, however, the writer may briefiy discuss the
marketing of beef products. Therefore, animal science would be the primary
category, while marketing would be the secondary category. The experts then
discussed their findings and made a final decision on each article.
All 262 articles were placed into primary and secondary concept areas.
The largest category in the primary concept area was the markefing category
(n=69), which consisted of 26% of the stories. The plant science category was
the second largest primary concept area (n=44), representing 17% of the
agricultural news stories. The animal science category (n=43) consisted of 16%
of the stories in the primary concept area. The natural resources category (n=36)
contained 14% of the news stories, while the public policy group (n=34) consisted
of 13% of the news stories. The significance category (n=26) contained 10% of
42
the news stories, and the processing category (n=10) had the least amount with
4% of the agricultural news stories.
In the secondary concept area, the significance category (n=99) had the
most agricultural news stories with 38%. The plant science category (n=61)
represented 23% of the news stories. The animal science category (n=44)
characterized 17% of the news stories, while the markefing category (n=36) was
indicative of 14% of the sample. The smallest categories were the natural
resources category (n=10), the processing category (n=7), and the public policy
category (n=5), represenfing 4%, 2%, and 2% respectively. Table 4.5 indicates
this informafion.
Table 4.5: Concept Areas According to Frick et al. (1995)
Primary
%
Secondary
%
Significance
26
10
99
38
Animal Science
43
16
44
17
Plant Science
44
17
61
23
Natural Resources
36
14
10
4
Public Policy
34
13
5
2
Marketing
69
26
36
14
Processing
10
4
7
2
262
100
262
100
Category
TOTAL
43
Findings Related to Obiective Three
Objective three sought to categorize the sentences in the identified articles
using the Hayakawa-Lowry News Bias Categories. Report sentences (n=1,856),
which represented 55% of the total sentences, are factual, unbiased sentences,
and can be attributed or unattributed. Inference sentences (n=154), which
represented 5% of the total sentences, do not confirm the truth, and can be
labeled or unlabeled. Judgment sentences (n=1.245). which represented 37% of
the total sentences, are expressions of the writer's or quoted speaker's opinions.
Hayakawa states that reporters who write judgment sentences usually use bias
in their writing. Judgment sentences can be attributed, unattributed, favorable,
and/or unfavorable. The other sentences (n=105), which represented 3% of the
total sentences, are typically rhetorical quesfions and introductory sentences.
Table 4.6 shows the breakdown of sentence types.
Table 4.6: Sentence Types
Sentence Type
Report
"
Number of Sentences
t856
%
55"
Inference
154
5
Judgment
1,245
37
105
3
3360
lOO"
Other
TOTAL
44
Nine different categories make up the subcategories of the original
categories: report, inference, judgment, and other (Lowry. 1971).
Report
attributed sentences (n=755) are factual and attributed to the source. These
sentences represented 22% of the total sentences. The largest category was the
report unattributed sentences (n=1,101). representing 33% of the total
sentences. These sentences are factual, however, they are not attributed to a
source.
Inference labeled sentences are statements about the unknown based on
the known. Often, they are interpretafions or generalizafions of events. Labeled
inferences use "tip-ofT words that alert the reader that the information is
subjecfive to some extent (Lowry, 1971). These "fip-ofT words include appear,
could, may, perhaps, and possible. The inference labeled sentences (n=66)
represented 2% of the total sentences, the smallest of the nine categories.
Inference unlabeled sentences are statements about the unknown based on the
known.
Unlabeled inference statements are often interpretations or
generalizafions of events, excluding the "fip-ofT words that are present with
inference labeled sentences. According to Lowry (1971), unlabeled inferences
are considered more bias, due to the fact that the "tip-ofT is not used to alert the
reader.
Inference unlabeled sentences (n=88) represented 3% of the total
sentences.
Judgment attributed, favorable sentences are statements of the writer's
approval or disapproval of an event, person, object, or situafion that are
45
attributed to a source and favorable toward the subject. The judgment attributed,
favorable sentences (n=620) represented 18% of the total sentences. Judgment
attributed, unfavorable sentences are statements of the writer's approval or
disapproval of an event, person, object, or situation, and are attributed to a
source and unfavorable towards the subject. Judgment, attributed, unfavorable
sentences (n=351) consisted of 10% of the total sentences.
Judgment
unattributed. favorable sentences are statements of the writer's approval or
disapproval of an event, person, object, or situafion, and are not attributed to a
source, but are favorable towards the subject Judgment unattributed. favorable
sentences (n=190) represented 6% of the total sentences.
Judgment
unattributed, unfavorable sentences are statements of the writer's approval or
disapproval of an event, person, object, or situafion that are not attributed to a
source, but are unfavorable towards the subject. The judgment unattributed,
unfavorable sentences (n=84) category comprised 3% of the total sentences.
The other sentence category is all other sentences, including rhetorical
quesfions and introductory sentences. Other sentences (n=105) represented 3%
of the total sentences in the agricultural news stories. Table 4.7 shows the
breakdown of the nine sentence categories.
46
Table 4.7: Categories of Sentences
Sentence Categories
Number of Sentences
%
755
22
1,101
33
Inference Labeled
66
2
Inference Unlabeled
88
3
Judgment Attributed, Favorable
620
18
Judgment Attributed. Unfavorable
351
10
Judgment Unattributed. Favorable
190
6
Judgment Unattributed, Unfavorable
84
3
Other
105
3
3,360
100
Report Attributed
Report Unattributed
TOTAL
Findings Related to Objective Four
Objective four was to determine bias of judgment statements in the
agricultural news stories. Judgment sentences (n=1,245) represented 37% of
the total sentences.
Judgment attributed, favorable sentences (n=620) represented the largest
percentage of judgment sentences with 50% of the total judgment sentences.
Judgment attributed, unfavorable (n=351) had the second largest percentage of
judgment sentences, represenfing 28% of the total judgment sentences.
Judgment unattributed, favorable (n=190) consisted of 15% of the total judgment
sentences.
Judgment unattributed, unfavorable (n=84) was the smallest
47
category, represenfing 7% of the total judgment sentences found in the
agricultural news stories.
Overall, 78% of all judgment sentences were attributed to a source,
leaving 22% of the total sentences unattributed. Also, 65% of all judgment
sentences were favorable to the subject Therefore. 35% of the total judgment
sentences were unfavorable towards the subject. Table 4.8 shows the
breakdown of judgment sentences.
Table 4.8: Judgment Sentences
Judgment Sentences
Number of Sentences
%
Attributed. Favorable
620
50
Attributed. Unfavorable
351
28
Unattributed, Favorable
190
15
Unattributed, Unfavorable
84
7
TOTAL
1,245
100
Findings Related to Obiective Five
Objecfive five sought to compare the findings of the agricultural news
articles used in this study to non-agricultural news articles in a similar study by
Hess (1997) and Hagins (2000). The following tables depict this informafion.
Table 4.9 illustrates the comparison of the amount of Associated Press
wire service articles to those articles used for this study. Daily averages of the
48
number of articles are included as well. The number of articles posted in 1997
on the Associated Press wire service was 145, with a daily average of 7.25. In
2000, the Associated Press wire service posted 177, with a daily average of 8.85.
This indicates a 22% increase in the number of articles between 1997 and 2000.
In 2002, AgOnline posted 165 agricultural news articles, with a daily average of
7.17. AgDayta posted 222 agricultural news articles, with a daily average of
9.65, while AgWeb posted 434, with a daily average of 18.87.
Table 4.9: Comparison of Number of Articles in 1997, 2000, and 2002
1997
2000
2001
AP
AP
AgOnline AgDayta AgWeb
Number of Articles
145
177
165
222
434
Daily Average of Articles
7.25
8.85
7.17
9.65
18.87
Articles
Table 4.10 represents the comparison of the number of Associated Press
wire service articles placed in each agricultural literacy concept area as the
primary category in 1997 and 2000, versus the agricultural news stories retrieved
from the three websites in 2002 and placed in a primary category. In 1997, the
largest category in the agricultural literacy concept areas was the significance
category, representing 23% of the Associated Press wire service stories. In
2000, the plant science category was the largest concept area, indicafive of 21%
of the total stories. In 2002. the largest agricultural literacy concept area was the
49
markefing category, which categorized 26% of the total agricultural news articles
retrieved from the three selected websites.
Table 4.10: Comparison of Primary Concept Areas in 1997. 2000. and 2002
WWW
AP
Category
1997(%)
2000 (%)
2002 (%)
Significance
23
19
10
Animal Science
16
12
16
Plant Science
22
21
17
Natural Resources
6
5
14
Public Policy
3
8
13
Markefing
9
20
26
Processing
8
5
4
Miscellaneous
13
10
0
Table 4.11 represents the comparison of the number of Associated Press
wire service articles placed in each agricultural literacy concept areas as the
secondary category in 1997 and 2000, versus the number of articles retrieved
from the selected websites in 2002 and placed in a secondary category. In 1997,
the largest secondary category was the general agriculture and the processing
category, both represenfing 23% of the total stories. The general agriculture
category was again the largest category in 2000, along with the plant science
category, both indicative of 19% of the total stories. With the agricultural news
50
articles from the three websites, general agriculture was again the largest
category, representing 38% of the total articles.
Table 4.11: Comparison of Secondary Concept Areas in 1997, 2000, and 2002
^ ,
Category
AP
WWW
1997 (%)
2000 (%)
2002 (%)
General Agriculture
23
19
38
Animal Science
5
13
17
Plant Science
7
19
23
Natural Resources
1
3
4
Public Policy
10
8
2
Markefing
18
17
14
Processing
23
11
2
Miscellaneous
13
10
0
Table 4.12 represents the comparison of report, inference, and judgment
sentences that occurred in the Associated Press wire service articles in 1997 and
2000. as well as the agricultural news articles retrieved from the selected
websites. Report sentences represented 42% of the total articles in the 1997
study, while 46% was indicative of the report sentences in the 2000 study. As for
the articles collected from the Worid Wide Web, 55% of the sentences were
report sentences. Inference sentences represented 18% of the sentences in
1997, 25% in 2000, and 5% in 2002. Judgment sentences epitomized 26% of
51
the sentences in 1997, 23% in 2000, and 37% in 2002. The "other" sentences
showed up in 14% of the sentences in 1997, 6% in 2000, and 3% in 2002.
Table 4.12: Comparison of Sentence Types in 1997, 2000, and 2002
AP
Sentence Types
WWW
1997 (%)
2000 (%)
2002 (%)
Report
42
46
55
Inference
18
25
5
Judgment
26
23
37
Other
14
6
3
Table 4.13 illustrates the comparison of the number of sentences
occurring in each sentence category in 1997 and 2000. versus the number of
sentences occurring in the articles retrieved from the selected websites. The
sentences were divided into nine categories: (1) report attributed. (2) report
unattributed. (3) inference labeled, (4) inference unlabeled, (5) judgment
attributed, favorable, (6) judgment attributed, unfavorable, (7) judgment
unattributed, favorable, (8) judgment unattributed, unfavorable, and (9) other. In
both 1997 and 2000, the report attributed sentences represented 11% of the total
sentences. In 2002, 22% characterized the report attributed sentences. In 1997,
32% of the total sentences were report unattributed, 35% of the sentences in the
2000 study were report unattributed, and 33% of the total sentences in the 2002
study were report unattributed. Inference labeled sentences characterized 16%
52
of the total sentences in 1997, 7% in 2000. and 2% in 2002. Inference unlabeled
sentences were indicative of 9% of the total sentences in 1997.18% in 2000. and
3% in 2002. Judgment attributed, favorable sentences represented 6% of the
total sentences in 1997. 8% in 2000, and 18% in 2002. Judgment attributed,
unfavorable sentences characterized 9% of the total sentences in 1997, 8% in
2000, and 10% in 2002. Judgment unattributed, favorable sentences portrayed
1% of the total sentences in 1997, 3% in 2000, and 6% in 2002. Judgment
unattributed, unfavorable sentences depicted 2% of the total sentences in 1997,
4% in 2000, and 3% in 2002. The "other" sentence category represented 14% of
the total sentences in 1997, 6% in 2000, and 3% in 2002.
Table 4.13: Comparison of Number of Sentences Occurring in each Sentence
Category in 1997. 2000, and 2002
AP
Sentence Categories
WWW
1997 (%)
2000 (%)
2002 (%)
Report Attributed
11
11
22
Report Unattributed
32
35
33
Inference Labeled
16
7
2
Inference Unlabeled
9
18
3
Judgment Attributed, Favorable
6
8
18
Judgment Attributed, Unfavorable
9
8
10
Judgment Unattributed, Favorable
1
3
6
Judgment Unattributed. Unfavorable
2
4
3
Other
14
6
3
53
Table 4.14 shows the comparison of the number of judgment sentences
that occurred in the Associated Press wire service articles in 1997 and 2000.
versus those that occurred in 2002 from the articles retrieved from the selected
websites. The judgment attributed, favorable category represented 35% of the
total judgment sentences in both the 1997 and 2000 study, and 50% in the 2002
study. The judgment attributed, unfavorable sentences categorized 51% of the
total judgment sentences in 1997, 36% in 2000, and 28% in 2002. The judgment
unattributed, favorable category depicted 5% of all judgment sentences in 1997,
12% in 2000, and 7% in 2002. The judgment unattributed, unfavorable sentence
category represented 9% of all judgment sentences in 1997, 17% in 2000, and
7% in 2002.
Table 4.14: Comparison of Number of Judgment Sentences in 1997, 2000, and
2002
AP
Judgment Sentences
WWW
1997(%)
2000 (%)
2002 (%)
Attributed, Favorable
35
35
50
Attributed, Unfavorable
51
36
28
Unattributed, Favorable
5
12
15
Unattributed, Unfavorable
9
17
7
54
CHAPTER V
CONCLUSIONS AND RECOMMENDATIONS
The purpose of this study was to evaluate the coverage of agriculture
available by popular agriculture websites on the Worid Wide Web for one
calendar month. The following objecfives were formulated to accomplish the
purpose of this study:
1. To identify all the articles written about agriculture on the most popular
agricultural websites on the Worid Wide Web for a selected month;
2. To categorize Worid Wide Web articles into agricultural literacy
concept areas;
3. To categorize the sentences in the identified articles using the
Hayakawa-Lowry News Bias Categories;
4. To determine bias of judgment statements in the identified articles; and
5. To compare findings from agricultural publicafions to that of nonagricultural publicafions (Hess, 1997; Hagins, 2000).
Summary
This study was conducted by collecting agricultural news articles from
three selected agricultural websites (AgDayta, AgOnline, and AgWeb) for a given
month. There were 821 articles retrieved from the three websites for the month
of January 2002. A random sample was taken from the population (n=262).
Each article was placed in an agricultural literacy concept area by a panel of
55
three experts. The panel read the articles and placed each individual sentence in
one of the nine Hayakawa-Lowry News Bias Categories. A total of 3,360
sentences were coded.
Conclusions Related to Obiective One
Objecfive one sought to identify all the articles written about agriculture on
the most popular agricultural websites on the Worid Wide Web for a selected
month. There were 821 articles collected from AgDayta, AgOnline, and AgWeb.
A random sample was taken, and 262 articles were used for the study. There
were 3,360 sentences in the 262 articles to code. The following conclusions
were made as a result of this study:
1. On average, there were about 7 articles a day posted on the selected
websites.
2. AgWeb provides the most agricultural coverage out of the three
agricultural websites. AgWeb posted 434 articles in January 2002.
AgDayta posted the second most articles with 222 articles in January
2002. AgOnline posted 165 articles in January 2002.
Conclusions Related to Obiective Two
Objective two sought to categorize Worid Wide Web arficles into
agricultural literacy concept areas. The concept areas used were developed by
Frick et al. (1995). The categories were: (1) significance, (2) animal science. (3)
56
plant science. (4) natural resources. (5) public policy. (6) markefing. and (7)
processing. All articles were placed in one of these seven categories as a
primary and secondary concept area. The following conclusions were made as a
result of this study:
1. There is a diverse range of topics written about agriculture and posted
on the selected websites.
2. The most frequenfiy written about topic during January 2002 was the
markefing category, with 26% of the articles represenfing this category.
3. The least frequent written about topic during January 2002 was the
processing category, with 4% of the articles represenfing this category.
Conclusions Related to Objective Three
Objecfive three sought to determine the level of bias in the identified
articles. The articles were read and evaluated by a panel of three experts. Each
sentence in the articles was placed in one of the following four sentence types:
(1) report, (2) inference. (3) judgment, or (4) other. The experts then placed the
sentences in one of the nine expanded categories: (1) report attributed, (2) report
unattributed, (3) inference labeled, (4) inference unlabeled, (5) judgment
attributed, favorable, (6) judgment attributed, unfavorable, (7) judgment
unattributed, favorable, (8) judgment unattributed, unfavorable, and (9) other.
The following conclusions were made as a result of this study:
57
1. A majority of the sentences were report statements, which are factual
and verifiable sentences. Report sentences characterized 55% of the
total sentences. These sentences are desirable, and report sentences
should become more frequent.
2. Inference sentences, which are subjecfive and immediately verifiable
sentences, represented a mere 5% of the total sentences. These
sentences should be avoided when wrifing about agricultural topics.
3. Agricultural reporters are using their opinions when wrifing agricultural
articles, and these are referred to as judgment sentences; expressions
of the writer's or quoted speaker's opinions. Thirty-seven percent of
the sentences were judgment sentences. Agricultural reporters should
refrain from including their personal opinions when reporting about
agricultural issues in order to paint a more accurate picture of
agriculture.
4. The "other" sentence category represented a small portion of the
sentences. Only 3% of the total sentences were included in the "other"
category, which are normally rhetorical quesfions and introductory
statements.
5. The agricultural reporters that wrote the articles used in this study
wrote more report sentences than any of the other categories.
Therefore, a factual image of agriculture is being conveyed.
58
6. In the report category, there were more report sentences not attributed
to a source than were attributed to a source. Twenty-two percent of
the total sentences were report attributed, while 33% of the total
sentences were report unattributed.
More sentences should be
attributed to a source.
7. The inference labeled and inference unlabeled categories were very
close, represenfing 2% and 3%, respecfively. These sentences should
be avoided. Agricultural reporters are limifing the use of inference
sentences.
8. The most frequenfiy used sentence type in the judgment category was
judgment attributed, favorable, representing 18% of the total
sentences. Therefore, there were more attributed judgment sentences
than there were unattributed judgment sentences. Also, there were
more favorable judgment sentences than there were unfavorable
judgment sentences.
Conclusions Related to Obiective Four
Objecfive four sought to determine bias of judgment statements in the
identified articles. The following conclusions were made as a result of this study:
1. Agricultural reporters are using their personal opinions when wrifing
about agriculture.
59
2. This study concluded that agricultural reporters are wrifing more
posifive bias towards agriculture than negative bias. Half (50%) of the
judgment sentences were attributed and favorable. Sixty-five percent
of all judgment sentences were favorable, while 35% were
unfavorable.
3. Also, agricultural reporters are attribufing to a source more often than
not. Seventy-eight percent of all judgment sentences were attributed
to a source.
Conclusions Related to Obiective Five
Objective five sought to compare findings from agricultural publicafions to
that of non-agricultural publicafions (Hess, 1997; Hagins, 2000). The following
conclusions were made as a result of this study:
1. There is substanfially more agricultural news articles available on the
three selected websites as compared to the Associated Press wire
service.
2. When a comparison between primary and secondary agricultural
literacy concept areas was made, the three studies were fairiy similar.
3. Agricultural reporters write more report and judgment sentences, and
less inference and other sentences.
4. The report attributed category was higher for agricultural reporters, and
the report unattributed category was similar across the three studies.
60
5. Agricultural reporters write much less inference sentences, as
compared to the Associated Press wire service articles.
6. Agricultural reporters write more judgment attributed sentences and
more favorable sentences, showing a more posifive bias towards
agriculture.
Recommendafions
The results of this study show the importance of agricultural literacy for
reporters. The following recommendafions were made as a result of this study:
1. The results of this study express the importance of agricultural literacy
for all groups of society. The general public needs a clear view of the
agricultural industry in order to make logical decisions about the
industry.
Reporters of agriculture should have a background
knowledge of the agricultural industry, or of the industry that they
report about News providers should hire reporters with an agricultural
background to report about agriculture.
2. Reporters should be aware of the accuracy of the information they
report so that they provide the general public with factual informafion
by wrifing report sentence and attribufing to a source.
3. This study should be replicated every other year in order to stay
cun-ent on the progress of agricultural reporters.
61
4. This study should be expanded into other modes of the media,
including radio, television, newspaper, etc.
5. This study could addifionally expand to other industries, for example,
the medical industry.
6. Due to the fact that agricultural websites portray a more accurate view
of agriculture, as seen by the number of report sentences in this study,
agricultural communicators should make these arficles available
through general news sources, such as the Associated Press wire
service.
7. It appears in this study that reporters with an agricultural interest or
background are more favorable towards agricultural topics, as
compared to the Associated Press wire service stories used by Hess
(1997) and Hagins (2000). Determining what factors affect reporters'
favorable or unfavorable attitudes towards a topic should be
researched further.
62
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68
APPENDIX A
SENTENCE BREAKDOWN
69
Table A . I : Sentence Breakdown
Article
Category
RA
RU
IL
lU
JAF
JAU
JUF
JUU
0
1
Markefing
4
4
0
0
3
4
0
1
0
2
Animal
3
13
1
6
10
5
14
6
3
3
Markefing
1
1
0
0
0
0
0
0
0
4
Plant
4
3
1
1
6
7
0
1
0
5
Policy
3
0
0
2
2
1
0
0
1
6
Animal
1
5
0
1
0
0
0
0
0
7
Plant
1
6
0
0
1
0
1
0
0
8
Agriculture
3
0
0
0
1
0
0
0
0
9
Animal
4
3
0
0
6
0
3
0
0
10
Resources
7
5
0
1
2
1
1
0
1
11
Resources
1
3
0
0
7
0
0
0
0
12
Resources
3
2
0
0
1
2
0
0
0
13
Agriculture
4
13
0
1
6
1
2
0
0
14
Markefing
0
3
0
0
0
0
0
0
0
15
Resources
4
1
0
0
4
0
2
0
0
16
Policy
5
2
0
0
0
0
0
0
0
17
Agriculture
0
6
1
0
1
1
0
0
0
18
Agriculture
1
1
0
0
0
0
1
0
0
19
Marketing
2
2
0
0
0
0
0
0
0
20
Plant
3
2
0
1
7
2
0
0
0
21
Agriculture
1
21
1
1
1
2
5
2
0
22
Animal
6
5
1
0
1
0
0
0
0
23
Resources
3
2
0
0
4
1
0
0
0
24
Markefing
7
1
0
0
5
6
0
0
0
25
Markefing
2
1
0
0
14
1
0
0
0
26
Policy
0
2
2
0
0
0
0
0
0
27
Agriculture
4
3
0
1
0
0
0
0
^
70
Table A.I: Confinued
Article
Category
RA
RU
IL
lU
JAF
JAU
JUF
JUU
0
28
Markefing
2
7
0
0
1
0
0
0
1
29
Resources
0
2
0
0
0
0
0
0
0
30
Processing
9
0
0
0
0
4
0
0
1
31
Animal
0
6
0
0
0
0
0
0
1
32
Plant
1
2
0
0
0
0
0
0
0
33
Animal
2
0
0
0
0
1
0
0
0
34
Resources
2
2
0
0
0
0
0
0
0
35
Processing
1
2
0
0
0
0
0
0
0
36
Markefing
4
31
0
0
3
1
13
0
3
37
Markefing
2
0
2
1
4
3
0
0
1
38
Plant
2
4
0
1
2
1
0
0
0
39
Animal
0
1
0
0
0
6
0
1
0
40
Policy
6
1
0
1
0
0
0
0
0
41
Markefing
8
0
0
1
0
0
0
1
0
42
Animal
0
6
0
0
2
0
0
0
0
43
Marketing
0
3
0
0
0
0
1
0
1
44
Agriculture
3
1
0
0
0
0
0
0
0
45
Policy
2
2
1
0
3
8
0
1
0
46
Marketing
0
0
0
0
0
4
0
0
0
47
Animal
1
8
0
1
3
0
1
0
0
48
Processing
1
2
0
0
0
0
0
0
0
49
Policy
2
2
0
0
4
0
1
0
0
50
Marketing
5
2
0
1
4
1
2
2
2
51
Animal
3
19
1
2
8
2
0
0
1
52
Animal
11
0
0
0
25
3
0
0
2
53
Marketing
3
3
0
0
0
0
0
0
0
54
Resources
2
1
0
0
0
1
0
0
0
71
Table A . I : Confinued
Article Category
RA
RU
IL
lU
JAF
JAU
JUF
JUU
0
55
Markefing
4
1
0
2
4
3
0
0
1
56
Markefing
1
1
0
0
1
0
0
0
0
57
Processing
1
4
0
0
1
0
0
0
0
58
Resources
1
8
0
0
3
0
1
0
0
59
Animal
0
3
0
0
8
0
9
0
0
60
Marketing
2
1
0
0
1
2
0
1
0
61
Marketing
6
18
0
0
2
0
7
2
0
62
Plant
9
12
0
1
6
1
8
0
1
63
Markefing
1
2
0
1
5
0
1
0
0
64
Policy
1
4
0
2
1
4
0
0
0
65
Policy
1
1
1
1
0
2
0
0
0
66
Resources
7
2
0
0
11
1
0
0
5
67
Markefing
0
0
0
0
2
4
0
2
0
68
Processing
17
1
0
0
1
5
0
1
1
69
Animal
3
0
0
1
0
1
0
0
0
70
Policy
2
0
1
0
3
2
1
0
1
71
Policy
2
3
1
0
2
0
0
0
0
72
Markefing
1
4
0
0
2
0
0
0
0
73
Markefing
2
0
0
1
1
0
1
0
5
74
Markefing
0
0
1
0
5
1
0
1
1
75
Agriculture
2
0
0
2
0
0
0
0
0
76
Policy
4
4
0
0
6
5
0
0
1
77
Markefing
2
1
0
1
3
6
0
0
0
78
Policy
7
0
0
0
0
0
0
0
0
79
Markefing
1
5
0
3
3
0
0
0
0
80
Animal
0
1
1
0
0
0
0
0
0
81
Agriculture
2
10
0
0
1
0
1
0
1
72
Table A . I : Confinued
Article
Category
RA
RU
IL
lU
JAF
JAU
JUF
JUU
0
82
Plant
3
11
3
8
0
0
3
4
0
83
Markefing
1
6
0
0
0
0
0
0
1
84
Plant
5
6
0
0
2
0
0
0
0
85
Markefing
3
6
0
0
0
0
0
0
1
86
Resources
1
1
0
0
0
0
0
0
0
87
Agriculture
5
3
0
3
4
0
0
1
0
88
Agriculture
1
2
0
1
0
0
0
0
0
89
Resources
1
4
0
0
1
0
1
0
0
90
Policy
2
8
0
0
4
1
1
0
0
91
Marketing
4
0
0
0
0
0
0
0
0
92
Policy
1
1
1
1
1
0
0
0
0
93
Marketing
1
0
0
4
0
0
4
2
0
94
Agriculture
0
5
0
0
0
0
0
0
0
95
Animal
0
3
0
0
0
0
0
0
0
96
Policy
3
2
0
0
1
1
0
0
0
97
Markefing
1
0
0
2
0
1
0
0
0
98
Policy
2
2
0
0
1
2
1
1
0
99
Markefing
2
3
0
0
1
0
0
0
0
100
Marketing
1
10
3
3
1
1
2
2
0
101
Animal
3
14
1
0
8
1
1
0
0
102
Plant
0
2
0
0
3
0
0
0
0
103
Markefing
4
6
0
0
0
0
0
1
0
104
Animal
4
12
0
1
4
1
2
0
2
105
Animal
4
2
0
0
3
1
0
0
0
106
Policy
4
0
0
0
5
1
0
0
0
107
Markefing
0
1
1
0
4
2
2
0
0
108
Marketing
8
38
0
4
6
0
6
1
0
73
Table A . I : Confinued
Article
Category
RA
RU
IL
lU
JAF
JAU
JUF
JUU
0
109
Policy
6
1
0
0
2
6
0
2
0
110
Plant
1
4
0
0
2
0
0
0
0
111
Policy
4
0
0
1
3
9
0
0
0
112
Markefing
0
3
0
1
4
0
0
0
0
113
Animal
2
3
1
0
3
4
0
0
0
114
Markefing
5
9
0
0
6
2
0
0
0
115
Animal
3
3
2
0
0
2
0
0
0
116
Policy
5
5
0
0
3
0
0
0
0
117
Resources
1
0
0
0
3
0
1
0
0
118
Resources
10
0
2
1
17
8
1
0
6
119
Plant
2
7
0
0
7
1
0
0
0
120
Animal
1
1
1
0
0
0
0
0
0
121
Resources
1
6
0
0
2
0
0
0
0
122
Policy
5
5
0
0
1
4
0
0
0
123
Agriculture
4
0
0
2
4
6
0
0
0
124
Animal
4
7
0
0
2
0
1
0
0
125
Markefing
2
0
2
0
6
6
0
0
0
126
Plant
6
3
0
0
1
2
0
0
0
127
Plant
4
4
0
0
4
5
1
1
1
128
Marketing
0
10
2
1
0
0
0
0
0
129
Agriculture
1
6
1
0
0
0
0
0
0
130
Plant
14
2
0
0
11
6
0
0
1
131
Resources
1
1
0
0
1
0
0
0
1
132
Plant
1
2
0
0
0
0
0
0
0
133
Plant
0
1
0
0
4
0
0
1
0
134
Marketing
6
5
0
0
1
0
1
0
0
' 135
Marketing
5
4
2
0
0
3
0
0
0
74
Table A.1: Continued
Article
Category
RA
RU
IL
lU
JAF
JAU
JUF
JUU
0
136
Marketing
1
9
0
0
0
0
2
0
4
137
Animal
2
4
1
0
2
0
0
0
0
138
Marketing
3
1
0
1
2
4
0
0
0
139
Animal
2
6
2
1
0
1
1
0
0
140
Plant
0
10
0
0
5
1
0
0
0
141
Marketing
2
3
0
0
3
5
0
0
0
142
Resources
6
1
1
0
0
5
0
0
2
143
Marketing
5
1
0
0
3
0
0
0
0
144
Resources
2
0
0
0
0
0
0
0
0
145
Plant
6
28
4
1
3
0
6
16
14
146
Plant
1
2
0
0
0
0
0
0
1
147
Animal
6
5
0
0
2
0
1
1
0
148
Resources
10
5
0
0
0
0
0
0
0
149
Marketing
2
2
0
0
1
0
0
0
0
150
Marketing
3
1
0
0
2
3
0
0
0
151
Marketing
2
3
0
0
0
1
0
1
0
152
Policy
0
1
0
0
0
2
0
0
0
153
Animal
3
3
0
2
0
4
0
1
0
154
Policy
3
7
0
1
2
0
0
0
0
155
Resources
1
11
1
0
1
0
2
0
0
156
Resources
7
19
0
0
12
1
6
0
0
157
Markefing
0
3
0
0
4
0
0
0
0
158
Plant
9
16
0
0
2
1
0
3
4
159
Markefing
0
1
0
0
3
0
0
0
0
160
Processing
1
2
0
0
0
0
0
0
0
161
Plant
1
0
0
0
9
4
0
1
1
162
Plant
0
5
1
1
0
0
1
0
0
75
Table A.1: Confinued
Article
Category
RA
RU
IL
lU
JAF
JAU
JUF
JUU
0
163
Processing
4
2
0
0
0
3
0
0
0
164
Plant
3
0
0
0
2
0
1
0
0
165
Plant
11
1
0
0
2
3
0
0
0
166
Plant
2
11
0
0
3
6
0
3
2
167
Marketing
2
1
0
0
3
0
0
0
0
168
Plant
4
7
0
0
2
2
1
1
2
169
Animal
2
2
0
1
0
0
0
0
0
170
Policy
1
2
0
0
0
0
0
0
0
171
Markefing
1
4
1
0
0
1
1
1
0
172
Animal
11
6
0
0
7
1
2
1
1
173
Policy
0
7
0
0
3
4
0
0
0
174
Resources
8
0
0
0
5
1
0
0
0
175
Plant
0
4
1
1
3
0
2
0
0
176
Plant
6
1
0
0
6
1
0
0
0
177
Plant
4
3
0
0
0
0
1
0
0
178
Resources
7
10
0
0
3
1
1
0
0
179
Processing
1
4
0
0
0
2
0
2
0
180
Agriculture
0
4
0
0
0
0
0
0
0
181
Animal
0
6
0
0
0
0
0
0
0
182
Policy
0
0
0
0
4
3
0
0
0
183
Animal
14
10
0
1
3
0
1
0
0
184
Marketing
1
1
1
1
3
2
1
0
1
185
Plant
3
1
0
0
0
2
0
0
0
186
Marketing
3
3
0
0
0
0
0
0
0
187
Plant
2
4
0
0
0
0
1
0
0
188
Animal
7
5
0
0
0
2
0
0
0
189
Plant
3
5
0
°
0
0
0
0
0
76
Table A.1: Confinued
Article
Category
RA
RU
IL
lU
JAF
JAU
JUF
JUU
0
190
Plant
2
10
0
0
6
0
0
0
0
191
Markefing
1
10
0
0
0
0
0
0
1
192
Animal
1
3
0
0
5
2
0
0
0
193
Agriculture
4
0
0
0
9
4
0
0
3
194
Plant
3
11
0
0
0
0
3
0
0
195
Plant
8
2
0
0
5
2
0
0
0
196
Resources
1
1
0
1
0
0
0
0
0
197
Animal
1
8
0
0
0
0
1
0
0
198
Policy
5
5
0
0
1
2
1
0
0
199
Resources
0
4
0
1
1
0
1
0
0
200
Animal
3
1
0
0
2
4
0
0
0
201
Resources
2
4
0
0
0
4
0
0
0
202
Agriculture
3
7
0
2
3
11
1
5
3
203
Plant
1
6
0
0
4
0
0
0
0
204
Animal
3
4
1
0
1
0
0
1
0
205
Marketing
1
1
0
0
1
1
0
0
0
206
Agriculture
0
1
0
0
1
10
0
0
0
207
Animal
0
7
3
0
0
0
3
1
1
208
Agriculture
9
24
1
1
22
3
16
2
5
209
Plant
4
2
0
0
1
4
1
1
0
210
Policy
2
2
0
0
2
0
0
0
0
211
Agriculture
3
2
0
0
7
2
0
0
0
212
Plant
1
1
0
0
1
0
0
0
0
213
Animal
2
4
0
0
0
0
0
0
0
214
Animal
3
8
0
0
0
0
0
0
0
215
Plant
4
2
0
0
2
0
0
0
2
216
Plant
0
7
0
0
0
0
6
0
0
77
Table A.1: Confinued
Article
Category
RA
RU
IL
lU
JAF
JAU
JUF
JUU
0
217
Animal
1
1
0
0
0
1
0
0
0
218
Markefing
0
4
0
0
0
0
0
0
0
219
Resources
3
1
0
0
2
0
1
0
0
220
Animal
1
1
1
0
2
1
0
0
0
221
Policy
1
1
0
0
2
0
0
0
0
222
Agriculture
0
3
0
0
0
0
0
0
1
223
Markefing
1
2
0
0
3
0
1
0
0
224
Markefing
0
3
0
0
2
1
0
1
0
225
Policy
1
9
1
0
3
14
0
2
0
226
Agriculture
2
5
0
0
3
1
0
0
0
227
Resources
1
3
0
0
7
6
2
0
1
228
Plant
10
1
0
0
1
3
3
0
0
229
Animal
0
3
0
0
0
0
0
0
0
230
Markefing
15
0
0
0
0
0
0
0
0
231
Resources
4
6
0
0
3
1
1
0
1
232
Markefing
1
2
0
0
0
1
1
0
0
233
Plant
2
11
0
0
0
0
0
0
0
234
Markefing
4
5
0
0
0
0
0
0
0
235
Markefing
4
3
0
0
0
0
0
0
0
236
Resources
14
3
0
0
15
2
0
0
0
237
Markefing
1
4
0
0
2
1
0
0
0
238
Processing
2
2
0
0
3
1
0
0
0
239
Resources
2
6
2
1
4
1
0
0
2
240
Animal
5
8
0
0
2
0
2
0
0
241
Resources
5
2
0
0
2
0
1
0
1
242
Resources
2
1
0
0
0
0
0
0
0
243
Marketing
0
2
0
0
0
0
1
0
0
78
Table A.1: Confinued
Article
Category
RA
RU
IL
lU
JAF
JAU
JUF
JUU
0
244
Animal
3
0
0
0
0
2
0
0
0
245
Policy
2
0
0
0
0
0
0
0
0
246
Plant
5
0
0
0
4
4
0
0
0
247
Agriculture
0
3
0
0
0
0
0
0
0
248
Policy
2
1
1
0
2
0
0
0
0
249
Marketing
9
12
0
0
9
1
2
1
1
250
Markefing
0
2
0
0
0
0
0
0
1
251
Agriculture
4
2
0
0
5
3
0
1
0
252
Resources
1
1
2
0
2
3
0
0
0
253
Policy
2
3
0
0
0
0
1
0
0
254
Agriculture
0
3
0
0
0
0
0
0
0
255
Markefing
0
17
2
0
4
0
1
0
0
256
Agriculture
1
5
0
0
6
0
0
0
0
257
Resources
5
2
0
0
0
0
1
0
0
258
Resources
1
3
0
0
0
8
1
0
1
259
Plant
0
5
0
1
4
0
0
0
0
260
Policy
3
6
0
0
1
3
0
0
0
261
Processing
2
6
0
0
0
0
0
0
0
262
Markefing
2
3
0
0
2
0
1
0
0
755
1,101
66
88
620
351
190
84
105
rOTAL
79
APPENDIX B
REPORT SENTENCES
80
EXAMPLES OF REPORT SENTENCES
Report Attributed Sentenneis
•
USDA's Foreign Ag Service reports as of January 18, 98% of Argenfina's
record 11.3 million hectares of soybeans were planted.
•
Reporting their fourth quarter and full-year financial results. Corn Products
International, Inc., noted challenges of 2001, and the importance of foreign
markets in 2002.
•
US and Canadian manufactures of organic fiber products have seen their
sales grow 22% annually over the past five years, with non-clothing items,
such as linens and personal care products, experiencing 39% growth,
according to the 2001 Manufactures' Market Survey from the Organic
Trade Associafion.
•
The Renewable Fuels Associafion today announced the domesfic ethanol
industry set an annual producfion record of 1.77 billion gallons in 2001, up
neariy 10% from 2000 and 20% from 1999.
•
Pigs have a built-in safeguard to prevent over sfimulation by growth
enhancers and other supplements. Mills said.
Report Unattributed Sentences
•
First quarter sales totaled $5.86 billion, compared to $1.77 billion last year.
•
Cun-ently, the United States exports 5.76 million tons of corn to the nafion.
•
Proper manure management also keeps nitrogen and phosphorous from
pollufing groundwater and surface water.
•
A soybean plant with crinkly bean will exhibit wrinkles on its canopy, giving
the plant a sickly appearance.
•
The decision to declare Britain as FMD-free means the country can
request the end of internafional trade sancfions imposed during the
country's FMD crisis.
81
APPENDIX C
INFERENCE SENTENCES
82
EXAMPLES OF INFERENCE SENTENCES
Inference Labeled Sentences
•
On the export front, several factors are evolving that could bring better
export business for U.S. wheat, most prominent being the entry of China
into the Worid Trade Organizafion.
•
Just as Brazil's producfion surged following the dramafic depreciation of
the Real in 1999, the decline of Argentina's cotton producfion may be
reversed in the next few years.
•
Each could render the job done, but had drawbacks.
•
All or a portion of the acreage under contract may be included in an
extension, but no new acreage may be added.
•
It looks as if the Senate will consider an economic sfimulus bill before
returning to a considerafion of a new farm bill.
Inference Unlabeled Sentences
•
With such a dramafic drop in producfion, many had expected wheat prices
to be significantiy higher than what was observed for 2001.
•
The Senate returns to work today, and many want to know when the farm
bill will come up for debate.
•
The hearing on the case is likely to be scheduled for April 2002.
•
By law, growers can begin planting the Lower Rio Grande Valley's 2002
cotton crop on Feb. 1, but cotton exports are hoping they hold off for a few
weeks.
•
Any farmer that raises soybeans knows that prices continue to be much
lower than we'd like to see them.
83
APPENDIX D
JUDGMENT SENTENCES
84
EXAMPLES OF JUDGMENT SENTENCES
Judgment Attributed. FavnrahiP
•
Reduced wastage through controlled feeding can make hay bales go
further, says David Davis, superintendent of the University of Missouri
Forage Systems Research Center.
•
Advances in cloning technology are making cloned pig organs easier to
transplant into humans, reports an Edinburgh-based research firm.
•
Purdue University researchers are unlocking possible ways to extend the
benefits of a feed addifive that makes pigs meatier, according to a news
release form the University.
•
Educafion is key, said Larry Howard, NU extension educator in Cuming
County.
•
Workshop attendees will take away other valuable informafion as well,
Obermeyer said.
Judgment Attributed. Unfavorable
"There are problems and there is no clear-cut way to solve them," said
David Sieck, NCGA spokesman.
At both nafional and local cotton meefings, Norman said he's noficed a
drop in the number of grower's participafion, a refiecfion of the country's
weakening agricultural economy.
Monsanto says cotton producers should steer away from imitation Roundup Ready products, DTN's cotton editor Mike McGinnis reports from the
Beltwide Cotton Conference in Afianta.
"The current state of affairs is causing biotech firms in Europe to flee," said
Larson.
Grassley said if USDA wanted to change the rate, it should have been
done "months ago," not after producers have made planfing decisions.
85
Judgment Unattributed. Favorable
•
The European Union has fightened its beef labeling rules to address
consumers' fears about bovine spongiform encephalopathy (BSE) and
give them more detailed informafion about the beef they buy.
•
The obvious benefits include reducfion of Styrofoam waste in landfills.
•
Producers also need to get feed readily accessible in the calving area and
be sure equipment and the calving barn are clean and ready for use.
•
A salvaged truck seat gives the tractor operator a comfortable ride.
•
The best planting rate is 20 to 25 bushels of sprigs per acre.
Judgment Unattributed. Unfavorable
•
The decline in export sales was cited as proof of the argument that U.S.
sales will drop off in the coming months as South American supplies begin
coming into the market.
•
The problem isn't new, but becomes pronounced when there is crop
damage due to weatiier. insects or disease.
•
The Senate also has problems in that our two greatest foes are in
posifions of leadership in the Republican Party.
•
The new law also threatens bulk exports of corn to Mexico.
•
Determining how soybeans develop crinkly bean is a challenge, even for
specialists.
86
APPENDIX E
"OTHER" SENTENCES
87
EXAMPLES OF "OTHER" SENTENCES
"Other" Sentences
•
Skepfics said it couldn't be done.
•
Grassley sees the schedule different.
•
Normally a few hours~or somefimes minutes—in a mall sends me racing
for the car.
•
"In other words, if the dollar were 10 percent lower, would the level of real
demand stay unchanged, and would the price of soybeans rise 10
percent?" Tierney suggests.
•
"You can't starve a profit out of a cow" but "you can feed away your
profits."
88
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