Keeping Everyone Buoyant: The Care Work of Women Faculty and Research Staff during COVID-19
Bibliographic record
Abstract
The authors explore how the coronavirus disease 2019 (COVID-19) pandemic amplified a broad range of care practices for women in academia. Engaging the lived experiences of faculty and research staff members, the authors investigate the entangled impact of care on work-life productivity during the first year and a half of the global pandemic. Mixed-methods data include roundtable accounts focusing on the COVID-19 experiences of woman employees at a Canadian university with supplementary analyses from a related institutional survey. The findings demonstrate a triangulated configuration of care responsibilities: care directly associated with work, care outside of work without disruption to professional excellence, and pressures of self-care. The authors conclude by describing the “cruel optimism” of care that is at once rewarding but simultaneously diminishing to personal flourishing. This article contributes to analytical efforts to critically redefine care in higher education as an ambivalent set of laborious practices steeped in inequities.
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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.030 | 0.019 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 0.005 |
| 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".