Activating Allies: A transformative interdisciplinary study to support inclusive and equitable workplace practices
Bibliographic record
Abstract
Advancing equity, diversity, and inclusion (EDI) is needed in Canadian workplaces. Due to the complex nature of social change, multiple solutions are required to advance EDI. In this interdisciplinary and collaborative study, we defined and developed solutions to support workplace allyship — which I argue is needed to achieve a shift in workplace culture. First, I explored literature in disciplines such as rhetoric (communication), leadership, education, law, policy, and sociology. In exploring the literature, I identified the important roles that employees, leaders, and organizations have in contributing to change efforts and the urgency to advance change. \nAllyship is a practice of inclusion where — through listening, learning, and reflection on personal experiences, and privileges — people actively support historically marginalized persons and communities in achieving their full potential. Throughout the study, the phenomena of workplace allyship has been explored in support of five equity-deserving groups: women, Indigenous peoples, visible minorities, persons with disabilities, and 2SLGBTQIA+ people. Additionally, an intersectional and transformational mixed-methods approach was used throughout multiple phases of the study. \nAfter receiving research ethics approval, we conducted three data collection phases. In Phase 2, we interviewed 17 active allies and developed the Ally Activation change model. The Ally Activation model was then used to develop the Active Allies course in Phase 3. In Phase 3, we tested the Active Allies course with 26 participants in a Canadian engineering college, and in Phase 4, with 76 participants in the Canadian mining industry. This study has provided evidence as to how individuals can be trained to act as workplace allies who practice inclusion and leaders — potential allies with role privilege — can develop competencies and motivation to recognize inequities and remove systemic barriers. Our findings have implications for EDI researchers and practitioners, including on how to foster psychologically safe EDI learning environments, and how to reduce EDI backlash. Additionally, this study offers insights into why organizations and leaders should adopt trauma-informed approaches as part of their change efforts. This study provides evidence that organizations and leaders — through active allyship — can better support everyone to feel valued, to achieve their full potential, and to increase their likelihood of solving complex problems. And the time to take the next step towards transformation is now.
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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 | 0.017 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.011 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.003 | 0.006 |
| 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".