Bibliographic record
Abstract
Making Feminist Media provides new ways of thinking about the vibrant media and craft cultures generated by Riot Grrrl and feminism’s third wave. It focuses on a cluster of feminist publications—including BUST , Bitch , HUES , Venus Zine , and Rockrgrl —that began as zines in the 1990s. By tracking their successes and failures, this book provides insight into the politics of feminism’s recent past. Making Feminist Media brings together interviews with magazine editors, research from zine archives, and analysis of the advertising, articles, editorials, and letters to the editor found in third-wave feminist magazines. It situates these publications within the long history of feminist publishing in the United States and Canada and argues that third-wave feminist magazines share important continuities and breaks with their historical forerunners. These publishing lineages challenge the still-dominant—and hotly contested— wave metaphor categorization of feminist culture. The stories, struggles, and strategies of these magazines not only represent contemporary feminism, they create and shape feminist cultures. The publications provide a feminist counter-public sphere in which the competing interests of editors, writers, readers, and advertisers can interact. Making Feminist Media argues that reading feminist magazines is far more than the consumption of information or entertainment: it is a profoundly intimate and political activity that shapes how readers understand themselves and each other as feminist thinkers.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".