Part of the Solution? Indigenous Apprentices and the Unionized Building Trades
Bibliographic record
Abstract
There have never been more favourable conditions for drawing Indigenous workers into the unionized building trades. The construction industry needs to replenish and diversify its overwhelmingly white, male, and aging workforce to meet skilled labour demands in the next few decades, when major civil infrastructure, mining, and green energy developments are expected to occur in northern Indigenous territories. These projects will be mandated by impact benefit agreements to employ a significant number of Indigenous workers who will first need to be trained. At the same time, Indigenous peoples are the fastest-growing population in Canada and have shown a propensity for pursuing trades education. In recent years, Ontario’s largest building trade unions have taken significant steps to recruit, train, and employ northern Indigenous workers, including in Nunavut. In collaboration with various stakeholders, the unions’ efforts are starting to show positive results. But are their methods and goals informed by decolonization, reconciliation, and Indigenization? This article reflects on this question while examining the case of the International Union of Operating Engineers Local 793, which has been a leader among building trades unions when it comes to establishing relationships with Indigenous partners, training Indigenous workers, and contributing to their economic self-determination.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.031 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| 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".