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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".