A theory of Abject Appearance: Women elite leaders’ intra-gender ‘management’ of bodies and appearance
Bibliographic record
Abstract
In this article, we develop a theory of Abject Appearance to explain women elite leaders’ embodied identity work within a context of intra-gender relations. The theory of Abject Appearance illuminates a dynamic and dialectical process whereby women elite leaders ‘manage’ the ambiguities of their ‘in-between’ and ‘abject’ status. This process is understood as a possible material effect or consequence of women’s abjection in organizations. Women elite leaders hold power through their formal positions, yet remain marginalized in social relations because their feminine bodies are out of place in organizations. In a qualitative study with women elite leaders, we illustrate how the theme Fascination with bodies and appearance depicts a dialectical process of simultaneous disgust and attraction with women’s bodies and appearance. We discuss how this material effect of abjection may be played out through two embodied identity work strategies in an intra-gender context, namely: Shifting focus from the body and appearance and Achieving a professional balance. We offer insights into how women’s embodiment in elite leader roles may be constrained in a context of intra-gender relations. We suggest opportunities to strengthen women’s agency by raising awareness to the theory of Abject Appearance and women leaders’ associated body work.
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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 | 0.003 | 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 | 0.004 | 0.030 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".