Bibliographic record
Abstract
Abstract Dialect variation is perceived and encoded in everyday language situations. Studies in the field of folk linguistic enquiry which has come to be known as perceptual dialectology, pioneered in the 1980s by scholars such as Dennis Preston, showed that beliefs nonlinguists have about language variation can play a critical role in language maintenance and change. This paper is an attempt to rethink the issue of accent identification from the perspective of perceptual dialectology by discussing the methodological hurdles to overcome when assessing folk perception of dialects. Illustration comes from two recent studies tackling the perception of geolinguistics variation in Eastern Canada. A comparison of the most common data collection techniques such as mental mapping, dialect identification tests requiring informants to listen to voices of different degrees of dialect markedness and dialect questionnaires raises several issues that call for a diversification of research design including indirect attitude measurements, especially affective priming.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.043 |
| Scholarly communication | 0.014 | 0.025 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".