“<i>So cool, right?</i>”: Canadian English Entering the 21st Century
Bibliographic record
Abstract
Abstract A socially stratified sample—the Toronto English Corpus —together with the construct of apparent time (with speakers aged 10–90 years) reveal that certain features are declining, including future will , deontic have got to , possessive have got , intensifier very , and the sentence tag you know . On the other hand, some features are on the rise, including future going to , deontic have to , possessive have , intensifiers really and so , and sentences tags such as whatever, so , and stuff like that . The younger generation is pushing these changes forward more rapidly. While some developments date back hundreds of years in the history of English, they are not particular to Canada, and are consistent with research on other English corpora. Other changes appear to be progressing in a unique way in Canada, including deontic and possessive have . I argue that the broader socio-historical context is a critical factor: geographic and economic mobility as well as changes in communication technology may explain the rapid acceleration of certain types of linguistic change.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".