The Role of Work Experience Programmes in Shaping Employment Outcomes for Indigenous Peoples in Canada
Bibliographic record
Abstract
This study examines employment outcomes of Indigenous peoples in Canada using an expanded human capital framework that includes education, health and work experience, such as internships and cooperative programmes. Despite some improvements, Indigenous employment rates remain below those of non‐Indigenous Canadians, with disparities across First Nations, Métis and Inuit populations. Using data from the 2016 Aboriginal Peoples Survey, this research assesses the impact of work experience programmes on employment status and income. Results show participation in work experience programmes increases the likelihood of employment by 12 percent, while post‐secondary education and good health also improve employment prospects. Findings highlight the need to broaden human capital strategies to include work experience programmes. These results are of particular interest to policymakers in Canada and Australia seeking evidence‐based strategies to improve employment outcomes and reduce economic disparities among Indigenous populations. This study is the first to empirically assess the role of work experience in Indigenous employment outcomes in Canada and provides new evidence to support Indigenous workforce development through experiential learning and holistic human capital investment.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| 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".