The <i>Rassemblement National</i> on social media: the online rewards of gendered political speech for radical right politicians
Bibliographic record
Abstract
Social media has provided powerful tools for parties looking to grow their followings and spread their messages, and the radical right has made good use of these tools as they reach out to voters concerned with immigration. Women politicians’ online experiences remain highly gendered, raising questions about the potential for social media to facilitate their substantive representation. Using the Rassemblement National as a case study, I take up the question of how patterns of gender inequality on the radical right are perpetuated on social media and in interactions with online audiences. I analyse data scraped from the X (formerly called Twitter) accounts of RN politicians with negative binomial regression analyses and a theoretically informed computational analysis. I find that, while gender is not a significant predictor of online engagement, online audiences are particularly responsive to women when they comply with stereotypical gender performances. I argue that despite the promise of social media to open new opportunities of self-presentation and interaction for marginalized politicians, women on the radical right continue to be held to strict gendered stereotypes on social media and are rewarded when they comply with these same stereotypes.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".