Leading at the border : gender, sex and sexuality in hypergendered organizations
Bibliographic record
Abstract
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 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".