HOW A CONNECTIVE ACTION IS DISRUPTED IN RESTRICTIVE CONTEXTS? THE CASE OF DISMANTLING #RAPE ON PERSIAN TWITTER
Bibliographic record
Abstract
Having reduced the cost of political activism, social media has provided room for ordinary citizens to engage in politics, build networks, spread information, and resist oppressive regulation (Howard & Hussain, 2013; Margetts et al., 2016). The ideas of connective action (Bennett & Segerberg, 2012) and hashtag activism (Jackson et al., 2020) are recent endeavors to theorize such transitions. However, the existing literature has overemphasized the positive side of social media platforms, in particular Twitter, in challenging inequalities, as well as in giving voice to marginalized groups (Lindgren, 2019; Wonneberger et al., 2020). While scholars, to a lesser extent, investigated how social media are used to suppress online protest from a normative and more general standpoint (Gunitsky, 2015), the ways that a connective action could be disrupted, e.g., by state actors has not received much scholarly attention yet. This has become particularly important in recent years, as several governments across the globe have adopted new tactics to dismantle connective actions, such as the coordinated dissemination of fake news. This study sheds light on such disruptive processes by investigating how a connective action in the Iranian Twittersphere (revolving around #rape , i.e., Iranian #MeToo) was derailed.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".