Are we failing female and racialized academics? A Canadian national survey examining the impacts of the COVID‐19 pandemic on tenure and tenure‐track faculty
Bibliographic record
Abstract
The novel coronavirus 2019 (COVID-19) pandemic caused the abrupt curtailment of on-campus research activities that amplified impacts experienced by female and racialized faculty. In this mixed-method study, we systematically and strategically unpack the impact of the shift of academic work environments to remote settings on tenured and tenure-track faculty in Canada. Our quantitative analysis demonstrated that female and racialized faculty experienced higher levels of stress, social isolation and lower well-being. Fewer women faculty felt support for health and wellness. Our qualitative data highlighted substantial gender inequities reported by female faculty such as increased caregiving burden that affected their research productivity. The most pronounced impacts were felt among pre-tenured female faculty. The present study urges university administration to take further action to support female and racialized faculty through substantial organizational change and reform. Given the disproportionate toll that female and racialized faculty experienced, we suggest a novel approach that include three dimensions of change: (1) establishing quantitative metrics to assess and evaluate pandemic-induced impact on research productivity, health and well-being, (2) coordinating collaborative responses with faculty unions across the nation to mitigate systemic inequities, and (3) strategically implementing a storytelling approach to amplify the experiences of marginalized populations such as women or racialized faculty and include those experiences as part of recommendations for change.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".