Facilitating inclusion: Workplace allyship interventions to foster a practice of inclusion in the Canadian mining industry
Bibliographic record
Abstract
The Canadian mining industry is on the threshold of a social transformation as it seeks to diversify its workforce and supply the critical minerals required for the global energy transition. Employees and leaders can be engaged, trained, and empowered to adopt a practice of inclusion—also known as allyship—in order to support the required transformation. In this study, researchers engaged 76 participants from the Canadian mining industry in a four-week allyship training program. Our findings show that learners’ allyship competencies and motivations to act as active workplace allies progressed during the course. As a result, participants are better equipped and more likely to engage in conversations about equity, diversity, and inclusion with their peers, subordinates, and leaders. Our findings suggest that leaders have an important role to play in fostering inclusive environments and sustaining allyship behaviors in others. Additionally, we offer insights into why organizations and their leaders should consider trauma-informed approaches to support the attraction and retention of a diverse workforce—an indicator of the successful social transformation.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".