Academia in the Time of COVID-19: Towards an Ethics of Care
Bibliographic record
Abstract
The global COVID-19 pandemic is affecting people’s work-life balance across the world. For academics, confinement policies enacted by most countries have implied a sudden switch to home-work, a transition to online teaching and mentoring, and an adjustment of research activities. In this article we discuss how the COVID-19 crisis is affecting our profession and how it may change it in the future. We argue that academia must foster a culture of care, help us refocus on what is most important, and redefine excellence in teaching and research. Such re-orientation can make academic practice more respectful and sustainable, now during confinement but also once the pandemic has passed. We conclude providing practical suggestions on how to renew our practice, which inevitably entails re-assessing the social-psychological, political, and environmental implications of academic activities and our value systems.
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 | 0.081 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.039 | 0.140 |
| Scholarly communication | 0.035 | 0.024 |
| Open science | 0.003 | 0.033 |
| Research integrity | 0.024 | 0.041 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".