Bibliographic record
Abstract
In the five preceding chapters of this book, three aspects of Canadian English have been emphasized: its current status as one of many languages spoken in Canada; its historical roots in settlement patterns; and its principal modern characteristics, viewed from both comparative and variationist perspectives. In this final chapter, the main outlines of these analyses will be briefly summarized and tentative projections will be made into the future, in terms of both research on Canadian English and the possible future development of the language itself. The analysis of Canadian English presented here is based on both the author's own research and the work of others, especially Avis, Chambers, Clarke, De Wolf, Gregg, Scargill, Warkentyne and Woods, as well as Labov, Ash and Boberg (2006) and data from Statistics Canada. Citations of this research are made throughout the foregoing chapters; in the interest of concision, they will not be repeated here. The status, history and comparative analysis of English in Canada: a summary In Chapter 1, English was seen to be the most widely spoken of many Canadian languages, being the mother tongue of about 18 million Canadians (57 percent of the national population). Of the other languages, the most important is French, Canada's other official language. Speakers of French are concentrated almost entirely in Quebec, where English is a minority language, and in neighboring regions of Ontario and New Brunswick. Canadians also speak a wide variety of non-official languages.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.109 | 0.028 |
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".