Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it