Femmephobia versus gender norms: Examining women’s responses to competing and contradictory gender messages
Bibliographic record
Abstract
While women experience pressure to be more feminine/less masculine, recent years have documented their simultaneous experiences of feeling pressured to be more masculine/less feminine. This phenomenon can be linked to the concept of femmephobia – the way femininity is devalued and regulated across bodies, identities, and various social locations. Despite the accumulating research documenting the existence of femmephobia across identities, how women respond to these experiences remains unexplored. Thus, the current study examined women’s ( N = 97) responses to femmephobia. Participants were asked to describe whether (and how) they attempted to modify any of the feminine expressions they felt were targeted in the femmephobic incident they reported. Results of the thematic analysis supported the hypothesized response types of amplification, suppression, and no change to feminine expressions following the femmephobic incident. We found that women overwhelmingly suppressed their femininity in response to femmephobia. Strikingly, while some women reported not making any changes, almost none of the respondents reported amplifying their feminine expressions in response to a femmephobic incident (and the few who did were all LGBTQ+). We theorize these findings in the context of women’s gender policing more broadly, specifically the tension between gender norms (pressure to be more feminine/less masculine) and femmephobia (pressure to be less feminine/more masculine). We argue that the frequency of suppressing femininity in response to femmephobia suggests that, when faced with contradictory gender messages, femmephobia may be more influential than considered by previous 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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".