Labour Market Prospects for the Métis in the Canadian Mining Industry
Bibliographic record
Abstract
The objective of this report is to review the prospects for Métis employment in the mining industry brought upon by a looming wave of retirements; to determine potential barriers to Métis employment in the mining industry; and to identify actions and strategies that the Métis National Council (MNC) and Métis Aboriginal Skills and Employment Training Strategy (ASETS) agreement holders should adopt to take advantage of and overcome obstacles to employment opportunities in the mining industry. The Canadian mining industry accounted for somewhere between two and five per cent of nominal GDP in Canada – depending on which definition of the mining industry is used – in 2008. This industry, concentrated in rural and remote locations, represents an important potentialsource of employment for the comparatively large youthful and rural Métis population entering the labour market in the coming decades. The mining industry has unique locational dynamics and hiring practices, a highly productive and experienced but aging work force, and growth prospects that are heavily reliant on global demand. Skilled workers are needed to replace the mining industry’s soon-to-be-retired baby boomers and to replace other workers leaving the industry. The Métis have unique demographic characteristics, which could create competitive advantages for employment in the mining industry; however, they are at risk of being unable to take advantage of the upcoming job openings if they cannot meet the educational requirements for employment in that industry.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".