Bibliographic record
Abstract
The dearth of real-time studies of the histories of transatlantic English varieties can be attributed to the lack of readily accessible, electronic corpora. However, for Canadian English (CanE) we now have the Bank of Canadian English (BCE), which consists of c. 2.5 million words from written and spoken sources extending from 1505 to the present. As a lexicographic database, the BCE does not constitute a balanced corpus, but from the onset it was designed to serve as a tool for corpus linguistic study. This paper seeks to determine the utility of the BCE for such study with a test case: the use of the subjunctive in adverbial (if) clauses. After first establishing that CanE patterns with American English (AmE) in having higher rates of subjunctive use in the present-day, the paper analyzes historical data extracted from the BCE to show that CanE, like British English (BrE) shows an increase in subjunctive forms in the first half of the eighteenth century (perhaps the continued effects of prescriptivism) followed by a significant decline beginning in the second half of the century. The generally higher use of the subjunctive in both AmE and CanE may in fact be evidence of “colonial revival” rather than “colonial lag”. There is tentative evidence in the BCE of a twentieth-century revival of the subjunctive, which has also been postulated for AmE (Leech et al. 2009). The developmental patterns in the BCE thus parallel those found in much larger corpora and give us quite accurate information about the history of this particular post-colonial variety.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".