Indigenous language policies in Canada in the wake of Bill C-91: Report on a national colloquium at Glendon College, December 2019
Bibliographic record
Abstract
C oinciding with the United Nations'Year of Indigenous Languages (2019), the Centre for Research on Language and Culture Contact (CRLCC) at Glendon College hosted a national colloquium on Canada's Indigenous language policies in the wake of Bill C-91 in December 2019.This colloquium was the second such gathering of Indigenous and Settler scholars and activists.The first, in 2016, was York University's response to the calls to action of the Truth and Reconciliation Commission on matters related to the long-ignored subject of Indigenous language rights in Canada.That gathering contributed to the federal government's announcement, in December 2016, of its intention to introduce Canada's first-ever federal legislation in support of Canada's Indigenous languages.Bill C-91, the Indigenous Languages Act, became law in June 2019.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.031 | 0.004 |
| Scholarly communication | 0.008 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".