Hannah and her sisters: Theorizing gender and leadership through the lens of feminist phenomenology
Bibliographic record
Abstract
This article explores how feminist phenomenology can add conceptual richness to gender and leadership theorizing. Although some leadership scholars engage with phenomenological and existential inquiry, feminist phenomenology receives far less attention. By addressing this critical gap in the scholarship, this article illustrates how feminist phenomenology can enrich gender and leadership scholarship. Specifically, by engaging with the work of four women existential phenomenologists – Hannah Arendt, Simone de Beauvoir, Iris Marion Young, and Sara Ahmed – the rich diversity of phenomenological inquiry is explored. First, Arendt shows the benefits of conceptualizing leadership as collective action, rather than as concentrated in one person, or organization. Second, Beauvoir highlights how women’s situation, and potential, is affected negatively by gender hierarchy. Third, Young builds on Beauvoir’s work by exploring the ways in which female modality is limited by the social construction of gender. Finally, Ahmed takes phenomenology in a queer direction, showing how normative ways of thinking about sexuality are limiting to those who do not fit the dominant, familiar pattern. As well, the merits and limitations of feminist phenomenology are explored as they relate to gender and leadership theorizing, and suggestions for future research are made.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".