Bibliographic record
Abstract
Traditional dialectology took region as its primary and often its only independent variable. Because of numerous social changes, region is no longer the primary determinant of language variation, and contemporary (sociolinguistic) dialectology has expanded the number of independent variables. In Dialect Topography, we survey a representative population, and that population inevitably includes some subjects born outside the survey region. We want to know how these non-natives affect language use in the community. Admitting them thus requires us to implement some mechanism for identifying them in order to compare their language use to the natives. The mechanism is called the Regionality Index (RI). Subjects are ranked on a scale from 1 to 7, with the best representatives of the region (indigenes) receiving a score of 1, the poorest (interlopers) a score of 7, and subjects of intermediate degrees of representativeness in between. I look at three case studies in which RI is significant: bureau in Quebec City, running shoes in the Golden Horseshoe, and soft drink in Quebec City. These results introduce a new dimension to the study of language variation as a regional phenomenon and provide a framework for the integration of regionality as one independent variable among many in dialect studies. The RI provides, perhaps for the first time, an empirical basis for inferring the sociolinguistic effects of mobility.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".