Networked Publics, Networked Politics: Resisting Gender-Based Violent Speech in Digital Media
Bibliographic record
Abstract
This dissertation is a qualitative study of digital media that identifies and analyzes feminist responses to violent speech in networked environments across Canada and the United States between 2011 and 2015. Exploring how verbal violence is constitutive of and constituted by power relations in the feminist blogosphere, I ask the following set of research questions: How do feminist bloggers politicize and problematize instances of violent speech on digital media? In what ways are their networked interactions and self-representations reconfigured as a result of having to face hostile audiences? What modes of agency appear within feminist blogging cultures? This work engages with feminist theory (hooks, 2014; McRobbie, 2009; Stringer 2014), media studies (boyd, 2014; Lovink, 2011; Marwick 2013) and their intersections in the field of feminist media studies (Jane 2014; Keller, 2012). Drawing on interviews with the key players in the feminist blogosphere and providing a discursive reading of selected digital texts, I identify networked resistive strategies including digital archiving, public shaming, strategic silence and institutional transformations. I argue that feminist responses to violent speech are varied and reflect not only long-standing concerns with community building and womens voices in public context, but also emerging anxieties around self-branding, professional identity and a control over one's digital presence. This research underscores the importance of transformative capacities of networked feminist politics and contextualizes agentic modes of participation in response to problematic communication.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.028 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".