Tweeting about sexism motivates further activism: A social identity perspective
Bibliographic record
Abstract
Women, more so than men, are using social media activism to respond to sexism. However, when they do, they are also faced with gendered criticisms 'hashtag feminism' that may instead serve to silence them. Based on social identity theory, this research examined how women's social media activism, in response to sexism, may be a first step towards further activism. Two studies used a simulated Twitter paradigm to expose women to sexism and randomly assign them to either tweet in response, or to a no-tweet control condition. Both studies found support for a serial mediation model such that tweeting out after sexism strengthened social identity, which in turn increased collective action intentions, and in turn, behavioural collective actions. Study 2 further showed that validation from others increases the indirect effect of tweeting on behavioural collective action through collective action intentions, but group efficacy did not moderate any indirect effects. It was concluded that social media activism in response to sexism promotes an enactment of women's social identity, thereby mobilizing them to further action.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".