Skill Mismatch of Indigenous Peoples in Canada: Findings from PIAAC
Bibliographic record
Abstract
In the Canadian sample of the Programme for the International Assessment of Adult Competencies, lower scores within occupation groups are more common among Indigenous individuals (not living on reserves) than for non-Indigenous individuals. This may be interpreted as evidence of what economists call under-skilling, with no implications regarding current job performance, but which has been associated with increased vulnerability to job loss during economic downturns. Estimated under-skilling rates are higher for English or French literacy among First Nations men, for numeracy among First Nations women, and for both proficiency domains among Inuit women and men. Over-skilling is primarily associated with inefficient use of labour resources and reduced welfare. Controlling for demographic characteristics, we find no consistent statistically significant evidence that the odds of being over-skilled—that is, of having higher scores than those within the same occupation group—are different for Indigenous individuals compared with non-Indigenous individuals. In estimated wage equations, wage differences shrink when our over-skilling and under-skilling estimates are added to the controls, but First Nations differences remain negative and significant, particularly for men.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".