Academic Women’s Labour During the COVID-19 Pandemic: A Review and Thematic Analysis of the Literature
Bibliographic record
Abstract
The COVID-19 pandemic has impacted academic labour, with women being disproportionately negatively affected. This scoping review provides an exploratory snapshot into the corpus of literature investigating the impact of the pandemic on academic labour. We used a set of criteria to first identify the 86 titles from which we selected 45 as the data set. We analyzed the data on characteristics of location, investigative methods, publication information, and discipline. The findings showed that most of the data were global in context; used primarily qualitative methodologies; published in a wide variety of journals; and spanned diverse disciplines, including science and health, education, business, sociology, and political sciences. We then analyzed the data thematically. The themes we identified were gender inequity, identities and intersectionality, performing work-home binaries, and invisible labour. We added a fifth theme, lived experiences, consisting of women academics’ firsthand accounts. We consider this theme unique, despite its overlap with the other themes, because it is evidence of women academics telling their personal stories. We discuss how our findings show that pandemic conditions worsened existing inequities. The solutions most often cited in the data place emphasis and responsibility on the individual, but we argue that institutions should instead be responsible to redress inequities through improving workplace labour processes. Our research can aid future research on how policy theory can inform socially just policies and practices in the post-pandemic university.
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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 | 0.034 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.026 | 0.038 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 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".