Grand Challenges and Female Leaders: An Exploration of Relational Leadership During the COVID-19 Pandemic
Bibliographic record
Abstract
Managing grand challenges demands a relational leader who encourages collaboration, coordination, and trust with various stakeholders. Although leaders appear to play a critical role in addressing grand challenges, relatively little research exists about the factors that inform stakeholder perceptions of leaders during a grand challenge. To address this limitation, we integrate implicit leadership theory and gender role theory to consider stakeholders’ gender prescriptive expectations when evaluating leader effectiveness during the COVID-19 pandemic. We theorize that stakeholders advantage female leaders based on mental schemas of what is required in a pandemic—relational leadership—and stakeholders’ prescriptive expectations of female leaders as more relational. Using a laboratory experiment, we find that female leaders are perceived as more relational, and hence, more effective than their male counterparts. Our findings advance scholars’ and practitioners’ understanding of strategic leadership, stakeholder management, and grand challenges.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".