Using public procurement to improve apprenticeship outcomes and promote a more diverse construction workforce in British Columbia
Bibliographic record
Abstract
Governments have been exploring ways to obtain better value from their procurement expenditures by attaching additional employment conditions to their contract tender documents. These require successful bidders to provide training and jobs for members of local communities or equity groups. In 2018, the British Columbia government created a public corporation, BC Infrastructure Benefits (BCIB) to employ the construction workforce on major public infrastructure projects, establishing an ambitious 25% apprenticeship target. It sought to expand the labour pool through recruiting, training and employing more women, Indigenous workers and people with disabilities. To obtain union support and enlist their members as onsite apprenticeship mentors, BCIB negotiated a community benefits agreement (CBA) covering its 10 infrastructure projects. BCIB also established a payroll-based data system to track progress and facilitate timely interventions to improve training outcomes. To address racism, sexism and homophobia in worksites, BCIB created a mandatory, two-day Respectful Onsite Initiative orientation programme. By December 2024, its workers had logged 7.5 million onsite construction hours, making BCIB the second largest provincial construction employer. Its apprenticeship and training outcomes exceed the rest of the industry, confirming that public procurement is a valuable tool for promoting vocational education and training (VET).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".