Campus climate assessment and action: disaggregating the social work experience in Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it