Stop Signs: The Intersection of Interdental Fricatives and Identity in Newfoundland
Bibliographic record
Abstract
Investigating local linguistic norms to discover larger patterns of language behaviour has been standard practice in sociolinguistic study. Looking closely at socially salient variables reveals patterns that problematize accepted trajectories of variation as traditional and newly emerging sociolinguistic identities interact. This paper integrates findings from multiple complementary projects to describe the forces influencing the stopping of interdental fricatives (dis ting for this thing), a highly salient marker of Newfoundland English, in and around St. John’s, the province’s major city. In urbanizing communities multivariate analysis reveals variation patterns typical of dialect erosion: older men maintain traditional norms while younger women move toward the standard, especially in linguistically salient contexts. In the same communities, a timing-based approach finds that young women seem to be agentively inserting stopped forms, suggesting that they have adopted a system with fricatives as the default choice. When we contrast urban and rural communities and affiliations, we find a more complex pattern: style shifting is greatest among urban males and rural females. We posit that these seemingly divergent patterns result from efforts by speakers to position themselves within the local social landscape during a period of rapid social change.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".