Investing in Aboriginal Education in Canada: An Economic Perspective
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The objective of this paper is to summarize the research done by the Centre for the Study of Living Standards (CSLS) on the economic impacts of improving levels of Aboriginal education. Improving the social and economic well-being of the Aboriginal population is not only a moral imperative; it is a sound investment that will pay substantial dividends in the coming decades. In particular, Canada’s Aboriginal population could play a key role in mitigating the looming long-term labour shortage caused by Canada’s aging population and low birth rate. We estimate that complete closure of both the education and the labour market outcomes gaps by 2026 would lead to cumulative benefits of $400.5 billion (2006 dollars) in additional output and $115 billion in avoided government expenditures over the 2001-2026 period.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it