Closing the Aboriginal Education Gap in Canada: Assessing Progress and Estimating the Economic Benefits
Bibliographic record
Abstract
This report has two major goals. The first goal is to assess progress on the gaps in educational attainment and labour market outcomes between 2001 and 2011 and the consequences of any progress (or lack thereof) for the Canadian economy. The second goal is to produce updated estimates of the benefits of eliminating the educational attainment gap. Utilizing projections of the Aboriginal population in 2031 and data from the 2011 National Household survey, we estimate the effects of closing the educational attainment gap on Aboriginal labour market outcomes and national economic performance. We provide breakdowns of the benefits by province, sex, age, Aboriginal identity, registered Indian status, and residence on- and off-reserve. We project that the direct cumulative economic benefits to Canada of closing the educational attainment gap between 2011 and 2031 could be as large as $261 billion.
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How this classification was reachedexpand
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".