The Debate on First Nations Education Funding: Mind the Gap (Working Paper 49)
Bibliographic record
Abstract
It is at long last becoming part of the public discourse that improving living conditions and opportunities for First Nations communities in Canada is a national imperative. It is also widely recognized that the education is critical to fostering a better future for First Nations people. Yet, for many First Nations youth, particularly those on reserve, completing even high school is well beyond reach. The graduation rate of First Nations people living on reserve was 35.3 per cent as recently as 2011 compared with 78 per cent for the population as a whole. At the same time, the First Nations population is young and growing fast - in First Nations communities 49 per cent of the population is under 24 years of age compared to 30 per cent of the general population. Despite some incremental improvements in education success rates for First Nations students in recent years, the education gap between First Nations and the rest of the country is increasing. The concerns expressed in the 2011 Auditor General report continue to hold weight: "In 2004, we noted that at existing rates, it would take 28 years for First Nations communities to reach the national average. More recent trends suggest that the time needed may still be longer.
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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 | 0.054 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.028 | 0.027 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.051 | 0.040 |
| Insufficient payload (model declined to judge) | 0.028 | 0.005 |
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".