‘How do you get to Tim Hortons?’ Direction-giving in Ontario dialects
Bibliographic record
Abstract
Abstract In this study, we target the speech act of direction-giving using variationist sociolinguistic methods within a corpus of vernacular speech from six Ontario communities. Not only do we find social and geographical correlates to linguistic choices in direction-giving, but we also establish the influence of the physical layout of the community/place in question. Direction-giving in the urban center of Toronto (Southern Ontario) contrasts with five Northern Ontario communities. Northerners use more relative directions, while Torontonians use more cardinal directions, landmarks, and proper street names – for example,Go east on Bloor to the Manulife Centre. We also find that specific lexical choices (e.g.,Take a rightvs.Make a right) distinguish direction-givers in Northern Ontario from those in Toronto. These differences identify direction-giving as an ideal site for sociolinguistic and dialectological investigation and corroborate previous findings documenting regional variation in Canadian English.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".