STOP AND GO (AWAY): LINGUISTIC CONSEQUENCES OF NON-LOCAL ASPIRATIONS AMONG SMALL-TOWN NEWFOUNDLAND YOUTH
Bibliographic record
Abstract
TO UNDERSTAND THE LINGUISTIC CLIMATE of Newfoundland, one must look not only at the linguistic variation that exists among Newfoundland speakers, but also at what underlies and drives this variation. It is clear that social factors such as region play a part; Newfoundlanders from the West coast speak differently from those from the Southern Shore, etc. (Clarke 2010). But region cannot account for the differences we observe within individual speech communities. The variationist approach to sociolinguistics has described three main categories of social forces that contribute to linguistic variation: (1) who the speakers are (e.g., age, socioeconomic status, community, Labov 1966), (2) who they know and how they interact (social networks, communities of practice, Milroy 1987, Eckert 2000), and (3) who the speakers want to be (social aspirations, Chambers 2003, Van Herk, Childs and Thorburn 2009).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".