An Evidence-Based Approach to Covid-19 Pandemic Effects on Academics
Bibliographic record
Abstract
Abstract We discuss our evidence-based approach to understanding and addressing the gendered impact of the pandemic on academics at Queen’s University Belfast, a research-intensive Russell Group UK University. The study was a collaboration between the University-wide Queen’s Gender Initiative, researchers, and Human Resources. A staff survey ran from 23 September until 30 October 2020, assessing academic productivity and personal factors including caring responsibilities, wellbeing, and time spent working. Data from 537 academics showed that multiple challenges were experienced with most of the day spent on work and caregiving tasks. The majority of worktime comprised teaching, at a cost to research productivity and personal wellbeing. These patterns were accentuated for female academics. From this holistic approach to understanding academics’ challenges, recommendations were presented to the University’s Executive Board and other high-level institutional committees. An Action Plan of sustainable solutions designed to mitigate the pandemic effect focused on promotion, research, workload support, and wellbeing. Furthermore, the findings directly informed policies to enhance working life, particularly in new models of flexible working. In summary, we report methodology to integrate research and centralized efforts to address the pandemic’s impact on academics, using a gender lens and incorporating complementarity of work, home-life and wellbeing.
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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 | 0.286 | 0.429 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.017 | 0.010 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.021 | 0.012 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".