Podcasting and Resistance to Gender-Based Violence across Canada, the United States, and Mexico
Bibliographic record
Abstract
Using a comparative border studies approach, this chapter explores how podcasting has been used to examine, expose, and critique longstanding structures and crises of gender-based violence, specifically femicide and feminicide in Canada, Mexico, and the United States. As it focuses on how North American borders figure in podcasts’ discussion of gender-based violence, the chapter explains how the medium is an effective means of shedding light on the trans-border and transnational nature of gender-based violence of femicide and feminicide and argues that podcasting’s essential qualities are fundamental to its potential to expose and challenge cross-border gender-based violence in North America. The chapter analyses two podcasts, Forgotten: Women of Juárez , hosted by journalists Oz Woloshyn and Mónica Ortiz Uribe, which aired in 2020 on the iHeartMedia platform; and journalist Connie Walker’s (Cree) Missing & Murdered: Finding Cleo investigation, which aired in 2018 as a Canadian Broadcasting Corporation podcast. Each of these shows draws attention and responds to femicides in different ways, mobilizing the narrative techniques of investigative journalism alongside more context-specific and locally grounded storytelling strategies to directly engage listeners.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".