Platform Feminism: Feminist Protest Space and the Politics of Spatial Organization
Bibliographic record
Abstract
Platform Feminism: Feminist Protest Space and the Politics of Spatial Organization examines the relationship between platforms and feminist politics. This dissertation proposes a new feminist media theory of the platform that positions the platform as a media object that elevates and amplifies some voices over others and renders marginal resistance tactics illegible. This dissertation develops the term “Platform Feminism” to describe an emerging view of digital platforms as always-already politically useful media for feminist empowerment. I argue that Platform Feminism has come to structure and dominate popular imaginaries of what a feminist politics is. In the same vein, the contemporary focus on digital platforms within media studies negates attention to the strategies of care, safety and survival that feminists who resist on the margins employ in the digital age. If we take seriously the imperative to survive rather than an overbearing commitment to speak up, then the platform’s role in feminism is revealed as limited in scope and potential. Through a mixed methodological approach via interviews with feminist activists, critical discourse analysis of platform protest materials, critical discourse analysis of news coverage and popular cultural responses to transnational feminist protests and participant observation within sites of feminist protest in Toronto, this dissertation argues that the platform is a media object that is over-determined in its political utility for Feminist politics and action.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.032 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".