Cree Decision Making Concerning Language: A Case Study
Bibliographic record
Abstract
In 1993, nine Cree communities on the east coast of James Bay (Québec, Canada) and inland began work on a pilot project to use Cree as the language of instruction (CLIP) in two communities, and have continued to extend this so that now Cree is the main language of instruction up to grade four (the target level) in many of the communities. We describe the complex context of language choice in schools before CLIP was implemented. In our analysis, four important threads of concern were identified: (1) locus of control (who had power in the communities and schools); (2) economies of scale (how the resources to accomplish Cree-medium teaching were created); (3) community visions of language and education (the evolution of attitudes, particularly of parents, towards the pertinent languages and their uses); and (4) the role of literacies (changes in community members' expectations of what literacy in Cree and English were good for). Our conclusion is that no simple models of language use are likely to be adequate for explaining or predicting outcomes in such complex situations. Documenting these cases longitudinally and in many facets provides unique local micro-analysis against which other circumstances can be compared.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.022 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".