Bibliographic record
Abstract
This chapter reports on the status of heritage languages (HLs) in Canada in usage, in research, and in education. It begins with an overview of HLs in Canada and the current ethnolinguistic vitality (demographics, institutional support, and status) of these language varieties. This includes an overview of programs to teach HLs (or to use HLs as the medium of instruction) in primary, secondary, and post-secondary contexts. Census information is provided to profile the distribution of HL speakers across major cities and all the provinces and territories of Canada, and the status of the HLs. The next section surveys publications about HLs in Canada including overviews, studies from the domain of sociolinguistics (language variation and change) that rely on spontaneous speech corpora, acquisition studies employing experimental methodology, and research on pedagogical approaches, noting primary findings from each. Specific information is provided about heritage varieties of Cantonese, German, Greek, Italian, Inuktitut, Korean, Mandarin, Russian, Spanish, Tagalog, and Ukrainian.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.039 | 0.004 |
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".