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Record W4387779366 · doi:10.1177/15480518231208576

How Women Leaders’ Identities Coexist Through Public and Private Identity Endorsements

2023· article· en· W4387779366 on OpenAlexafffund
Alyson Byrne, Ingrid C. Chadwick

Bibliographic record

VenueJournal of Leadership & Organizational Studies · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsConcordia UniversityMemorial University of Newfoundland
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsIdentity (music)Construct (python library)Public relationsSocial psychologyAgency (philosophy)Social identity theorySociologyPsychologyPolitical scienceSocial group

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.347
Threshold uncertainty score0.867

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.003
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.443
GPT teacher head0.372
Teacher spread0.071 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2023
Admission routes2
Has abstractyes

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