Reply to John Landon's Position Paper Arlene Stairs The Professional Development of Native Educators: Context, Culture,
Bibliographic record
Abstract
and Language I am fortunate that my experiences in Canadian Native education include one exceptionally hopeful situation which is evolving strongly towards the mainstream and nonassimilative Native education model proposed by John Landon. The Inuit of Arctic Quebec provide this example of exceptional progress; an example which must be related to two unique historical-cultural circumstances as well as to the will and effort of educators and the population. First, Inuit in Arctic Quebec maintain the highest level of Native language use in Canada, including an Inuktitut basic literacy rate of close to 100 % (Stairs 1985). Relative geographic and economic isolation, and the fact that Quebec has two competing non-Native languages, are among the factors cited to explain such high Native language viability. Second, Native negotiation with eastern North Ameri-ca's demand for electricity led to the James Bay and Northern Quebec Agreement of 1976, which has been responsible for Inuit political and
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.035 | 0.050 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".