Campus climate assessment and action: disaggregating the social work experience in Canada
Bibliographic record
Abstract
Climate surveys hold the potential to advance equity in organizations, serving to generate quantitative data on the depth and breadth of climate-related issues, with its forte being those related to belonging, inclusion, and relationships. When administered in a university, it holds the potential to signal the need for improvements, as climate has been associated with engagement, motivation, wellbeing, and retention. The Faculty of Social Work, where an MSW and PhD program are located (Kitchener, Canada), conducted a climate survey in 2020. This article reports on the survey’s content, key findings, action outcomes, and provides recommendations for others considering such an initiative. The survey was a wake-up call for the department, with five concrete outcomes including establishing student caucus groups, a faculty capacity-development initiative to improve teaching, trainings to address microaggressions, improved integration of EDI into hirings, and campaigns to collect identity-based data for faculty and students. We also share two pending initiatives and two derailed initiatives. Recommendations emphasize the importance of disaggregating results to ensure that disparities are identified in the organization.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".