Women deserve better: A discussion on COVID‐19 and the gendered organization in the new economy
Bibliographic record
Abstract
Abstract It is often thought that large‐scale shocks to society (e.g., war, epidemics, financial collapses etc) equalize societal inequalities, however, we have witnessed a one‐in‐century pandemic (and the economic downturn it has triggered), widen rather than narrow an enduring global injustice: gendered organizations. With women bearing the brunt of school closures, mass lay‐offs and increase in care duties due to lockdowns, racialized women at increased risk of COVID exposure due to essential worker status, and men reaping the benefits of rapid, technological transformations of the economy—largely amplified by pandemic disruptions—it appears that white, masculine bodies and abilities in the workforce are inoculated from the perils of disaster. Equality matters, especially in times of crisis. Following this idea, the author draws on Joan Acker's “ideal worker” concept to demonstrate how pandemic disparities in the workforce are challenging organizational practices, expectations, and experiences worldwide to evolve. This article concludes with a call for workplace policy reforms as a means to advance gender parity goals, as it is critical to achieving organizational inclusivity, and overall, a thriving society and economy post‐pandemic.
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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 | 0.010 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.041 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".