The case of Tumblr: young people’s mediatised responses to the crisis of learning about gender at school
Bibliographic record
Abstract
This volume critically analyses political strategies, civil society initiatives and modes of representation that challenge the conventional narratives of women in contexts of violence. It deepens into the concepts of victimhood and agency that inform the current debate on women as victims.\n\n\n\nThe volume opens the scope to explore initiatives that transcend the pair abuser–victim and explore the complex relations between gender and violence, and individual and collective accountability, through politics, activism and cultural productions in order to seek social transformation for gender justice. In innovative and interdisciplinary case studies, it brings attention to initiatives and narratives that make new spaces possible in which to name, self-identify, and resignify the female political subject as a social agent in situations of violence. The volume is global in scope, bringing together contributions ranging from India, Cambodia or Kenya, to Quebec, Bosnia or Spain. Different aspects of gender-based violence are analysed, from intimate relationships, sexual violence, military contexts, society and institutions.\n\n\n\nRe-writing Women as Victims: From Theory to Practice will be a key text for students, researchers and professionals in gender studies, political sciences, sociology and media and cultural Studies. Activists and policy makers will also find its practical approach and engagement with social transformation to be essential reading.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.017 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".