The <i>Rassemblement National</i> on social media: the online rewards of gendered political speech for radical right politicians
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it