Bibliographic record
Abstract
Purpose The purpose of this paper is to revisit theoretical positions on gender and the implications for gender in management by building upon current research on doing gender well (or appropriately in congruence with sex category) and re‐doing or undoing gender and argue that gender can be done well and differently through simultaneous, multiple enactments of femininity and masculinity. Design/methodology/approach This is a theoretical paper. Findings The authors argue that individuals can perform exaggerated expressions of femininity (or masculinity) while simultaneously performing alternative expressions of femininity or masculinity. The authors question claims that gender can be undone and incorporate sex category into their understanding of doing gender – it cannot be ignored in experiences of doing gender. The authors contend that the binary divide constrains and restricts how men and women do gender but it can be disrupted or unsettled. Research limitations/implications This paper focuses upon the implications of doing gender well and differently, for gender and management research and practice, drawing upon examples of leadership, entrepreneurship, female misogyny and Queen Bee. Originality/value This paper offers a conceptualization of doing gender that acknowledges the gender binary, while also suggesting possibilities of unsettling it.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.025 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".