Bibliographic record
Abstract
In this article, the educational attainment of Indigenous peoples of working age (25 to 64 years) in Canada is examined. This diverse population has typically had lower educational levels than the general population in Canada. Results indicate that, while on the positive side there are a greater number of highly educated Indigenous peoples, there is also a continuing gap between Indigenous and non-Indigenous peoples. Data also indicate that the proportion with less than high school education declined, which corresponds with a rise of those with a PSE; the reverse was true in 1996. Despite these gains, however, the large and increasing absolute numbers of those without a high school education is alarming. There are intra-Indigenous differences: First Nations with Indian Status and the Inuit are not doing as well as non-Status and Métis peoples. Comparisons between the Indigenous and non-Indigenous populations reveal that the documented gap in post-secondary educational attainment is at best stagnant. Out of the data analysis, and based on the history of educational policy, we comment on the current reform proposed by the Government of Canada, announced in February of 2014, and propose several policy recommendations to move educational attainment forward.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".