Gender and leadership? Leadership and gender? A journey through the landscape of theories
Bibliographic record
Abstract
The purpose of this article was to examine the following three questions: Are women’s leadership styles truly different from men’s? Are these styles less likely to be effective? Is the determination of women’s effectiveness as a leaders fact‐based or a perception that has become a reality? Conclusions revealed: Question one: Yes, women’s leadership style is, at this point, different from men’s but men can learn from and adopt “women’s” style and use it effectively as well. In other words, effective leadership is not the exclusive domain of either gender and both can learn from the other. Question two: No, women’s styles are not at all likely to be less effective; in fact, they are more effective within the context of team‐based, consensually driven organizational structures that are more prevalent in today’s world. Question three: The assessment that a woman’s leadership style is less effective than a man’s is not fact‐based but rather driven, by socialization, to a perception that certainly persists. The inescapable reality is that, within the senior ranks of corporate north America (and elsewhere), women remain conspicuous by their absence.
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 | 0.014 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.006 | 0.050 |
| Scholarly communication | 0.014 | 0.021 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| 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".