“Eight Tory leadership candidates declare themselves feminists”: feminism and political campaigns
Bibliographic record
Abstract
This study examines the co-optation of feminism by politicians. Adopting a case study approach, we explore three contemporary leaders who declared themselves feminists during political campaigns: Canadian Prime Minister Justin Trudeau, Mayor of London Sadiq Khan, and British Prime Minister Boris Johnson. We analyse how these politicians communicated a feminist identity during and after electoral campaigns. Drawing from a thematic analysis of 503 international mainstream news articles, Instagram feeds, and selected Tweets we demonstrate how all three politicians reduce “feminism” to a neoliberal political theory that is neither radical nor revolutionary, but primarily focused on redistributive inequalities and ideals of getting women at the table. In this regard, we argue that the ambition to address “gender pay gaps” or achieve “gender-balanced cabinets” is inadequate in the project of gender emancipation. Using the concept of co-optation, we contribute to a critical interrogation of feminism in the mainstream media by providing insight into how self-identified male politicians engage with neoliberal, popular, and mainstream feminist rhetoric and action which provides them with both cultural and political capital. This draws attention to the context of political practice, the factors that shape such politicians’ behaviour in relation to hegemonic, neoliberal feminism, as well as the consequences of their actions on attaining gender justice.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.018 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".