<scp>John Edwards</scp> (ed.), <i>Language in Canada</i>. Cambridge & New York: Cambridge University Press, 1998. Pp. xvi, 504.
Bibliographic record
Abstract
This volume is meant as a companion piece to three previous volumes published by Cambridge on language in various parts of the English-speaking world (the volume on the United States, edited by Charles Ferguson and Shirley Brice Heath, appeared in 1981, followed in 1984 by one on the British Isles edited by Peter Trudgill, and in 1991 by a volume on Australia edited by Suzanne Romaine). This collection contains 26 short articles, divided into three sets. The first set attempts to provide an overview of sociolinguistic issues in Canada from historical, demographic, and policy perspectives. The second set treats aboriginal languages and the two official languages, French and English; this set includes two articles on language teaching – restricted, however, to the teaching of international languages, mainly as first languages, and to the teaching of French as a second language through immersion methods. The third set offers language profiles of each of Canada's ten provinces, as well as of its two (now three) territories. The organization of the book is meant to provide different angles on sociolinguistic issues in Canada, but unfortunately the result too often is that material is either repeated or consistently left out.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.052 | 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".