Responsibilization of Female Academics for Mental Health
Bibliographic record
Abstract
In the neoliberal university, increasing demands have been placed on academics, resulting in heavier workloads, longer work hours, and workplace stress. Female academics face additional challenges not experienced by their male colleagues, including fewer leadership opportunities, lower salaries, and greater caring responsibilities both within the university and at home, often resulting in increased burnout out and academic attrition. Those with intersecting identities are even further negatively impacted by these issues. These inequities were only deepened by the COVID-19 pandemic. While previous research has documented the issues with a “one size fits all” approach to mental wellness implemented by universities, there is minimal research exploring the role played by university policies, practices, and programs in the responsibilization of vulnerable faculty for their own well-being in the academy. To address this, we are conducting a policy scan that explores how 24 universities across Canada, the United Kingdom, and Australia address mental health/wellness and equity, diversity, and inclusion (EDI), with a specific focus on how these universities responsibilize vulnerable faculty members for their own well-being.
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.
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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.003 |
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".