Bibliographic record
Abstract
Dr. Rebecca Stringer studied Art History and Criticism and interned at the Peggy Guggenheim Collection before completing a Ph.D. in Political Science at Australian National University. Her research examines theories, meanings and politics of victimhood in modern and neoliberal times, and her book Knowing victims: Feminism, agency and victim politics in neoliberal times (2014) examines the neoliberal transformation in how we talk about and conceptualize victimization. Rebecca's other publications trace the dynamics of victim politics in contexts including Indigenous policy in Australia, the government of drug use, rape law, and the rise of precarious academic work, and her current projects examine the origins of victimology and the visual culture of victimhood. Rebecca teaches and supervises in the areas of feminist theory and critical victimology at the University of Otago. She has been a visiting fellow at the University of Alberta, the University of Sydney, and Flinders University, and has presented her research at conferences and events in Aotearoa/New Zealand, Australia, North America, the UK, and Europe._Rebecca was co-editor, with Hilary Radner, of Feminism at the movies: Understanding gender in contemporary popular cinema (2011), and with Damien Riggs she co-edits the book series Critical perspectives on the psychology of sexuality, gender, and queer studies, which publishes scholarship challenging the way psychology has traditionally thought about bodies, identities, and experience, with a focus on sex, gender, and sexuality._
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.013 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.089 | 0.023 |
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".