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Record W769605711

Leading at the border : gender, sex and sexuality in hypergendered organizations

2010· dissertation· en· W769605711 on OpenAlexaboutno aff
Michèle A. Bowring

Bibliographic record

VenueFigshare · 2010
Typedissertation
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsnot available
Fundersnot available
KeywordsHuman sexualityGender studiesPolitical scienceSociology
DOInot available

Abstract

fetched live from OpenAlex

The leadership literature, although very well established, has paid limited attention to the differences between people, even when it has examined the different ways in which women and men may lead. In particular, any attention to those differences has been as if sex and gender are the same, while sexuality has been ignored.
\nThe conceptual framework for my thesis comes from Butler’s (1990) work on the performativity of gender and her discussion of the heterosexual framework. Therefore, in this thesis I attempt to address the deficiencies above by answering the following questions:
\nHow do/can people construct identities that transcend the heterosexual matrix?
\nAs people construct their identities as leaders, do they seek to reconcile all their other identities into a coherent whole with their identity as a leader?
\nTo what extent are leadership, sex, gender and sexual identities ‘fixed’ or ‘static’?
\nHow do queer or borderline identities intersect with leadership?
\nI explore these questions by interviewing 34 leaders of varying sexes, genders and sexual orientations. These respondents were active and retired members of the military and nursing in the UK, Canada or the US.
\nPerhaps the most significant finding was that for these respondents, their body trumped the other two aspects of identity, i.e., their gender and their sexuality, when developing and enacting their leadership within these hypergendered organizations.

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)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.347
Threshold uncertainty score0.934

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.1870.001

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 head0.121
GPT teacher head0.353
Teacher spread0.232 · 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.

Study designNot applicable
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

Citations3
Published2010
Admission routes1
Has abstractyes

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