Indigenous and Non-Indigenous Unemployment, Employment and Participation Rates Through 2024: Education is Strongly Related to These Three Rates
Bibliographic record
Abstract
The overall employment picture for the Indigenous population living off reserve was worse in 2024 than in 2023, and it was also worse in 2023 compared to 2022. They were also worse for non-Indigenous people in 2024, compared to 2023. Unemployment rates were higher, and employment and participation rates were lower. Employment, unemployment, and participation rates have been more favourable for the non-Indigenous population than for the Indigenous population. This has been the case in every year, except one, since 2007, which is the earliest that the data has been available. However, the educational level achieved is a critical factor. When educational levels are higher, unemployment rates are lower, and employment and participation rates are higher. Further, the participation rates for the Indigenous population were higher for each comparable education level than for the non-Indigenous population from 2007 through 2024, except for four years.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".