Bibliographic record
Abstract
As women’s participation becomes increasingly visible within far-right movements, the question of agency is often brought up; are women in the far-right agents? Although this question has been explored before, only a limited sect of this literature has analyzed this phenomenon through a feminist theoretical lens. This is notable considering much of women’s participation stems from an initial rejection of feminist values, often citing feminism as being the root cause of their lack of quality of life. Throughout this paper, I will argue that women’s agency in far-right movements can be better explained through the application of Simone de Beauvoir’s theoretical framework. I will situate this argument within a literature review that analyzes the previous understandings of women’s agency within the far-right. Following this, I will additionally present literature on hegemonic masculinity and its relationship to far-right women’s complicity. Furthermore, the strength of using de Beauvoir’s theoretical framework to analyze women of the far right will be asserted through an analysis of the case study of Ayla Stewart, an infamous far-right online influence. This paper ultimately aims to answer the following: How can de Beauvoir’s theoretical framework help better understand the agency of women within far-right political and social movements?
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".