COVID-19 and Caregiving IS Researchers: In the Same Storm, but not in the Same Boat
Bibliographic record
Abstract
In early 2020, reports emerged about the coronavirus disease of 2019 (COVID-19) pandemic having a negative effect on the productivity of female researchers who spent their time in lockdown taking care of their families but a positive effect on the productivity of male researchers who spent it writing more papers. We wondered if the pandemic had affected caregivers (mostly female) in the information systems (IS) discipline in the same way. If we found that it did, we hoped to be able to suggest what actions caregivers might take in response. As an approximate way to distinguish caregivers from non-caregivers in our analysis, we used gender. Our analysis yielded mixed results, but those results do suggest that the COVID-19 pandemic has had some negative impacts on IS researchers who are caregivers. We offer several recommendations to caregiving IS researchers for mitigating the effect that the pandemic has on their professional lives.
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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 | 0.020 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".