The social meaning behind linguistic variation has played a role in humanity since language was
commonplace. We can see this in many different ways. The “Boston” accent, for example, is a certain
type of linguistic variation that helps in forming a speaker’s perceived identity. It acts as a signifier of
where a person is from and can give others ideas about the personality, intelligence, age, etc. It seems
fairly common to us, but it wasn’t until Labov came along and noticed that some speakers used /r/ in
speech, and exhibited r-fullness in their speech. He found those who dealt with higher class customers
were already prone to more r-full speech but also when those from the lower class department store
were asked to repeat themselves they showed a tendency to be more r-full. Through his department
store study, Labov was able to demonstrate that when people paid more attention to their speech, they
began to “correct” themselves and speak more carefully. So, this linguistic trait was able to be classified
as being perceived as good, or prestigious, it also showed that these traits can be utilized, subconscious
or not, to effect the perception of the speaker.
Nancy Niedzielski in “The Effect of Social Information on the Perception of Sociolinguistic
Variables” compared Michiganders to Canadians and asked them to listen to two speakers, one used a
stereotypical Canadian accent, while the other used more American standard language. The
Michiganders, who use even more of the stereotypical Canadian traits than the actual Canadian
participants, identified more with the standard speaker than with the Canadian speaker. Niedzielski
found that some linguistic traits are subconscious and sometimes not even recognizable by the speaker.
But, it most definitely is being used by others to form their perception of the speaker.
By acknowledging that some linguistic traits are either changed or kept subconsciously, and are
often unrecognizable by the speaker (same study as last para) the linguist can choose to either make the
subconscious trait well known and therefore try to push the feature into the conscious (transforming it
from unmarked to marked), or study how these subconscious features effect how the speaker is
perceived by others. Kathryn Campbell-Kibler studied the latter in the form of the linguistic variable ING.
Campbell Kibler found that ING does carry social meaning but found that the perception of the speaker
using, or not using, it was not consistent. Campbell-Kibler found that overall, speakers that used ING
over IN were perceived as more intelligent/educated. What was shown though, is that this perception
did not correlate equally outside of the speaker’s general identity. In other words, a working class
speaker that uses IN is ranked lower than the same class speaker using ING, but a working class speaker
using ING was ranked as less intelligent than a not working class speaker using IN or ING. CampbellKibler also found that Southern speakers using ING were ranked higher than a standard working class
speaker using IN but the Southern speakers were ranked as less intelligent over all, regardless of their
use of ING. Kathryn Campbell-Kibler found and proved that the use of IN or ING effects the perception of
the speaker, but intentionally left out race.
My research will be to observe how listeners perceive intelligence based on ING versus IN and if
race has an effect. We already know that race influences how a listener perceives the speaker’ words
and sentences—expecting Mass when a white person says Mass, but expecting Mast when a black
person says Mass (Casasanto 2008)—but I want to find out if race influences a stigmatized linguistic
variable.
The most important aspects of my study include race, occupation and the IN/ING token.
Participants will be given the following information for each speaker: a photo of the speaker (not
necessarily of the actual speaker), race, occupation and a speech sample. They will be prompted to rank
the following from 1-5 with 1 being least and 5 being most which I will convert to a Likert scale (Drager
no date) (Mallinson et al) (based on the information they were given): intelligence, professionalism and
happiness. The only relevant ranking for my data collection will be intelligence.
Because the length of the survey shouldn’t be too long (in order to decrease the dropout rate
among participants and attract more), I will have two black subjects, two white subjects and I will have
four filler subjects (Drager no date) (Mallinson et al) (Di Paolo et al) (all male) to disperse throughout the
survey for a total of eight speakers. I will have one representative from the black speakers and one
representative from the white speakers from the highest ranked intelligence jobs: Cardiologist and
Orthopedic Surgeon, and one representative of each from the lowest ranked intelligence jobs: Janitor
and Garbage man, for a total of four subjects that I will use data from.
The occupations were chosen through a survey that I ran prior to the release of this survey. This
survey was created to find out the true perceived intelligence of the jobs that I will use for each speaker
without having to assume on behalf of the participants so results based on a misinterpretation of a
certain job’s perception by me will not occur (e.g. I think Accountants are perceived as more intelligent
than Doctors but in reality that is not the case. This could have skewed expected results because my
own baseless ideas of those jobs). I chose jobs with specific titles that fell under multiple class categories
like doctor, retail, food services, etc. The list of jobs included: Cardiologist, Orthopedic Surgeon,
Investment Banker, Accountant, Mailman, Garbage man, Cashier, Janitor, House Keeper and 8th Grade
Teacher. Each participant’s list of rankings was randomized so none of their ranking choices were
influenced by initial order. Orthopedic Surgeon and Cardiologist were the highest ranked jobs, while
Janitor and Garbage man were the lowest ranked jobs. These rankings were generated by 81
participants through Facebook solicitation, so a similar minded audience will be the majority of the
participants for the actual survey which means that it is reasonable to assume that these results hold
true for the data that will be collected.
The speakers will speak different sentences but each sentence will hold the same semantic
value. In other words, speaker 1 and speaker 2 will have different sentences but speaker 1’s sentence
will be “I saw my cat walking away.” and speaker 2’s sentence will be “I saw somebody walking the dog.”
Each sentence is different, but neither sentence would act in swaying the participant. This is instead of
having each speaker say the same sentence. The participant might recognize what variable is being
change, or the results might be skewed because hearing the same sentence spoken eight times in a row
will not elicit equal results for speaker 1 and speaker 8. I will be using a single white male speaker and a
single black male speaker for the four speakers whose data will be relevant. This will eliminate the
variable of the speaker’s voice inside of the two races.
The data I collect will be the intelligence ranking based on race, occupation and ING/IN. I will
look at the disparity between the white and black speakers of the same job/class status, the disparity
between the different classes within the same race (so, white upper class versus white lower class), and
the disparity between different classes and races (so, white upper class versus black lower class). This
will give me insight to how fiercely the linguistic feature is stigmatized, but also how much race effects
the stigmatization itself.