The Difference “Difference” Makes: Women and Leadership
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Why are women so dramatically underrepresented in formal leadership positions-and what can be done to improve the situation? This unique collection takes up these questions in the crucial practical concepts of law, politics, and business-the arenas in which women's leadership has the most public influence. Bridging the worlds of theory and practice, the essays in this collection bring new insights to long-standing questions about the difference gender difference makes, both in access to leadership and in its exercise. The contributors to this collection represent some of the nation's most distinguished women leaders and most respected scholars on women and leadership, and reflect a distinctive array of perspectives and backgrounds. Among others, they include former Congresswoman Patricia Schroeder; former NOW president Patricia Ireland; the Right Honorable Kim Campbell, former prime minister of Canada; and Judith Resnik, the Arthur Liman Professor of Law, Yale Law School. Written in accessible, lively prose, and informed by a wealth of scholarship and personal experience, this collection should appeal to a broad audience.
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.
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 | 0.002 | 0.001 |
| 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 | 0.006 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it