Femme interventions and the proper feminist subject: Critical approaches to decolonizing western feminist pedagogies
Bibliographic record
Abstract
As it currently stands, little academic attention has been paid to the systematic devaluation of femininity or femmephobia. By adopting “femme” as a critical analytic, this paper dislocates femininity from its ascribed Otherness and demonstrates how empowered femininities have been overlooked within gender studies. Femme, as the failure or refusal to approximate the patriarchal norms of femininity, serves as the conceptual anchor of this study and is used to examine how femmephobic sentiments are perpetuated within Contemporary Western Feminist (CWF) theory. This perpetuation is propped up by the thematic marginalization of empowered femininities from the texts chosen for gender studies courses, revealing a normative feminist body constructed through the privileging of identities that maintains femininity as white, middle-class, normatively bodied, and without agency. The excavation of an empowered feminine subject from the margins reveals the foothold of normative whiteness embedded within feminist pedagogies. Using a thematic analysis of how femininity is taken-up within textbooks used in gender studies courses, the current paper demonstrates how intersections of femininity have yet to be addressed within dominant Feminist theories. The femme—as a queer potentiality—offers a way of (re)thinking through the limitations of CWF theory and the paradoxical preoccupations with the absented femme.
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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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.011 | 0.070 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| 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".