“You Have to Feed the Beast”: Sexual Violence News Coverage in the Digital Age
Bibliographic record
Abstract
“You Have to Feed the Beast”: Sexual Violence News Coverage in the Digital Age examines how digital media and the working conditions of journalists shape sexual violence news coverage in Canada. This dissertation proposes that sexual violence news coverage is always both a labour question and a feminist theory question: journalistic working conditions overdetermine what kinds of, and whose, stories can be told. I argue that worker exploitation, the growing role of digital media, and the intensification of digital journalistic work play a fundamental role in shaping the experiences of survivors and those who interview and write about them. Further, my research argues that centering a feminist analysis of sexual violence and sexual violence news coverage reveals much about the working conditions of journalists today. Survivors of sexual violence and harassment find themselves harassed again to come out with a statement, to “break their silence,” and to make themselves available and transparent to overworked journalists for whom being profitable is increasingly an existential concern. In this dissertation, I analyse news stories, social media platform affordances, and interviews with journalists, as well as engage with the writing of survivors in creative “diffractions.” I treat sexual violence as fundamental to understanding journalism and the ways that unequal distributions of power, violence, believability, and evidence are central to journalism and to the effects of journalistic work on both those who practice it and those upon whom it is practiced.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.019 | 0.014 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".