Literacy, Numeracy, Technology Skill, and Labour Market Outcomes among Indigenous Peoples in Canada
Bibliographic record
Abstract
We use the 2012 Programme for the International Assessment of Adult Competencies to examine the relationship between information-processing skills, educational attainment, and labour market outcomes among Indigenous peoples in Canada. Relative to the non-Indigenous sample, we find negative earnings differentials, higher unemployment, and lower employment and labour market participation among Indigenous peoples, as well as important differences between First Nations, Métis, and Inuit workers. First Nations peoples show larger gaps in terms of earnings and employment outcomes. Moreover, Métis peoples show worse employment outcomes and negative earnings differentials in the upper part of the distribution. First Nations peoples also show sizable gaps in literacy, numeracy, and technology skill relative to the non-Indigenous sample. Not surprisingly, there is a positive relationship between information-processing skills and wages. However, the returns to skills are very similar for Indigenous and non-Indigenous peoples. That is, we find no evidence of economic discrimination. Once these skills are conditioned on, the earnings differentials decline. We also find that education can reduce skill and wage gaps, although the additional impact is small. The results imply the need to consider barriers to education faced by Indigenous peoples.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".