Newfies, Cajuns, Hillbillies, and Yoopers: Gendered Media Representations of Authentic Locals
Bibliographic record
Abstract
This paper investigates how popular media representations of Newfies, Cajuns, Yoopers, and Hillbillies maintain gender-based language stereotypes. 1 The authen-ticity of these locals is in part due to their language use; they are also the "best" speakers of the local variety. In addition, the stereotypes include the notion that the "best speaker " and the "authentic local " are male, and that the standard speaker and non-local is female, or males who do not fit traditional notions of masculinity (Schilling-Estes 1998). Media representations are key in shaping folk perceptions, and folk percep-tions of regional varieties are significant in reinforcing and maintaining language attitudes (Edwards 1982; Preston 2002). Likewise, folk perceptions of gendered language use help to maintain gender stereotypes. Schilling-Estes (1998) explains that language attitudes and gender identity are linked by the notion that the authen-tic local is "authentic " because he is the "best " speaker of the local variety. The best speaker is usually identified by his use of stereotypical linguistic features, and because these features are tied to "masculine " language, they not only carry the
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".