Bibliographic record
Abstract
Abstract This article traces the history of the minor complementisers as if , as though , and like (when they follow evidential verbs such as seem and look ) in Canadian English. By the 21st century, both as if and as though were rare in Canada, while like appeared to have become popular ( López-Couso and Méndez-Naya 2012b ). The Victoria English Archive ( D’Arcy 2011–2014 , 2015 ; Roeder, Onosson, and D’Arcy 2018 ) is used to map out the change in a combination of synchronic and diachronic spoken data. Results show that as if and as though are unusual even in the earliest speakers, which puts spoken Canadian English at odds with contemporaneous writing ( Brook 2014 ). However, this unexpected register difference may explain why the complementiser like caught on in North American dialects of English sooner and more readily than in the United Kingdom – where a robust as if and as though in speech would have remained barriers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".