Hashtag feminism in a blocked context: The mechanisms of unfolding and disrupting #rape on Persian Twitter
Bibliographic record
Abstract
Despite the growing body of literature on hashtag feminism in Western contexts, there is still a significant gap in our knowledge of the ways that feminist hashtag movements are developed in authoritarian societies. Moreover, we do not know much about the mechanisms by which a feminist hashtag movement is disrupted, particularly in non-democratic regimes. Drawing on and contributing to the hashtag feminism literature, we undertook a discursive approach to examine #rape on Persian Twitter to address these gaps. Findings showed that #rape was a space for Iranian users to share abusive experiences, but they went further to discuss barriers of disclosing sexual assault as well. Developing resistive strategies and raising awareness about other muted groups such as LGBTQIA+ were other discursive practices in articulating #rape. This article also pushes forward the existing literature on hashtag feminism by providing empirical analyses of the ways that a feminist movement is disrupted.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".