Radical Women? Investigating Gender Disparities and Women’s Representation on the European Radical Right
Bibliographic record
Abstract
Recent decades have seen an electoral resurgence of radical right parties in Europe. The increase in support for these parties has raised new and important questions about women’s role in radical right politics, as supporters, politicians, and leaders. I take up multiple aspects of gendered inequality on the radical right in Europe and find that women continue to be marginalized and tokenized on the radical right. My first dissertation paper examines the gender gap in radical right support in the Nordic nations – where, despite international reputations as gender egalitarian welfare paradises, the radical right has seen significant electoral success. Using data from the European Social Survey, and logistic regression decomposition analyses, I find that the gender gap in voting is largely driven by gender differences in anti-immigrant sentiment. I argue that anti-immigrant politics serve as a double-edged sword for the radical right, attracting some segments of the population and repelling others. My second paper investigates the normalization of gendered inequality on the radical right on social media. While social media has served as a powerful tool for political underdogs, including radical right parties, women’s experiences online are still deeply gendered. I analyse data scraped from the twitter accounts of Rassemblement National politicians with negative binomial regression analyses and a thematic analysis. I find that not only is the offline gender inequality of the radical right normalized online, but women on the radical right are held to strict gendered stereotypes on social media and are rewarded when they comply with these same stereotypes. Finally, I examine women radical right politician’s relative network positions in legislative contexts. Through a network analysis of parliamentary motion co-authorship, I find that radical right politicians are less cooperative and productive than politicians of other parties. This sheds light on why, even as these parties have made electoral gains, their legislative efforts have met mixed results. Unlike women in mainstream parties, the women of the radical right are less well-connected than their men cohorts. I argue that radical right women politicians experience a dual marginalization: as women in radical right politics, and as radical right politicians.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".