Leading the Fight Against the Pandemic: Does Gender Really Matter?
Bibliographic record
Abstract
Since the start of the ongoing coronavirus pandemic, the relationship between national women leaders and their effectiveness in handling the COVID-19 crisis has received much media attention. This paper scrutinizes this association by considering income, demography, health infrastructure, gender norms, and other national characteristics and asks if women's leadership is associated with fewer COVID-19 cases and deaths in the first few months of the pandemic. The paper also examines differences in the policy responses of leaders by gender. Using a constructed dataset for 194 countries, it uses a variety of economic and sociodemographic variables to match nearest neighbors. The findings show that COVID-19 outcomes, especially deaths, are better in countries led by women and may be explained by the timing of lockdowns. The study uses insights from behavioral studies and leadership literature to speculate on the sources of these gender differences as well as on their implications.HIGHLIGHTS COVID-19 offers a unique spotlight on the effectiveness of national leadership in crises.Little is known about how women versus men leaders manage national crises.Nearest-neighbor matching reveals women-led countries performed better in COVID-19 outcomes.Women leaders locked down their countries more quickly than their men-led neighbors.Women leaders also communicated in ways that were markedly different from men leaders.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".