Feminist and Queer Practices in the Online and Offline Activism of Occupy Wall Street
Bibliographic record
Abstract
While the Occupy movement has attracted popular and academic interest since 2011, there has been relatively little attention to the significance of feminist and queer practices and discourses to the movement. Drawing from feminist theory, social movement studies, and digital media studies, this paper presents empirical research conducted at Occupy Wall Street (OWS) that highlights feminist, queer, and trans contributions to OWS from its inception, with a focus on the Trans World Order Affinity Group (TOAG). Through its activities at Zuccotti Park as well at its website and on social media, TOAG has challenged gender norms, heteronormativity, and transphobia in both the physical and virtual domains of OWS, and been a key contributor to its online prominence. As such, the group offers an illuminating case study of the challenges and successes of realizing feminist and queer agency during key developments of a major social movement. More generally, this project provides an important corrective to the prominence of class in how Occupy has been discussed and conducted, pointing out how other axes of inequality have also stratified Occupy itself. In addition, it addresses central issues about new media and participation, including negotiating differences between diverse activist groups, the efficacy of digital activism, and the relationship between online and offline strategies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.021 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".