Supporting First Nations Secondary Students Studying Away From Home: A Case History of Policy Gone Awry
Bibliographic record
Abstract
In 2003, the Northern Nishnawbe Education Council (NNEC) invited proposals for a review of its support services for the secondary students that it sponsors. The author was the successful bidder on that contract and this is the story of the lessons that emerged from that work first in regard to educational policy in general and then in regard to First Nations education in particular. The single most important lesson for the larger world of educational policy, a literature replete with stories of implementation failure, is to “be careful what you wish for, because, against all odds, you just might get it!” Among the more important lessons for the Aboriginal and First Nations educational community are the dangers associated with preferential hiring policies that place unqualified people in professionally very demanding student-support and administrative roles, all the more so when the people in question, although ethnically and “racially” as well as legally “Indian,” lack any ties to the communities they serve.
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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 | 0.017 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.064 | 0.033 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.021 | 0.031 |
| 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".