Feminist futures in gender-in-leadership research: self-reflexive approximations to intersectional situatedness
Bibliographic record
Abstract
Purpose This paper introduces intersectional situatedness to develop inclusive analyses of leadership. Intersectional situatedness recognises the contextual and situated nature of experiences and their interaction with socially constructed categories of difference. Design/methodology/approach The paper draws on memory work by three feminist academics who situate their understandings and experiences of leadership as part of socio-historical contexts. Findings Understandings and experiences of leadership are multifaceted and benefit from being examined in their intersectional situatedness. This way, the simultaneity of visible and invisible disadvantage and privilege, which accumulate, shift and get reconfigured across the life course and are based on particular intersectional identity invocations, can be integrated into narratives about leadership. Research limitations/implications Interrogating gender-in-leadership adopting an intersectional situatedness helps to advance the field by embedding the recognition, problematisation and theorisation of situated difference as critical to understand leadership, its meaning and its practice in management and organisations. Practical implications In embedding intersectional situatedness in the analysis of leadership, more inclusive understandings of leadership are qualified that recognise differences positively and support changing the narratives around the meaning of “leader” and “good leadership”. Social implications Intersectional situatedness helps to identify tangible ways to see how inequalities impact women’s career progression to leadership and enable more nuanced conversations about privilege and disadvantage to advance feminist social justice agendas. Originality/value The paper reveals the narrow and restricted understandings of leadership and how this influences who is regarded as a legitimate leader. In addition, it adopts a methodology that is not commonly used in gender-in-leadership research.
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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 | 0.039 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.092 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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.
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".