How Women Leaders’ Identities Coexist Through Public and Private Identity Endorsements
Bibliographic record
Abstract
The development of a leader identity is considered essential for leadership success. Underlying this identity work is the belief that the social identity of being a leader is positive—something that leaders both privately endorse and want publicly conferred by others. However, this process is complex for women leaders who are simultaneously navigating the identity work of being women in male-dominated leadership positions. We conducted a qualitative investigation of women in senior leadership roles to examine how they construct a leader identity by managing private and public endorsements of both a leader and a female identity. Our results indicate that women leaders engage in leader and gender identity work whereby they actively manage how they privately self-endorse and publicly allow others to endorse their leader and female identities using identity hybridization. In so doing, they mix and recombine elements of both their leader and gender identities to construct a coherent female leader identity. Doing so allows them to benefit from the complementarity of these identities, while mitigating the risks associated with publicly and privately endorsing these two identities in tandem. This approach to identity hybridization allows women leaders to maintain a sense of agency, effectiveness, and authenticity in the face of identity tensions.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".