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Record W6958719416 · doi:10.7302/24276

Interview with Rebecca Stringer

2023· other· en· W6958719416 on OpenAlexaboutno aff

Bibliographic record

VenueDeep Blue (University of Michigan) · 2023
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsAgency (philosophy)PoliticsScholarshipCriticismFeminismQueerNeoliberalism (international relations)Cultural studiesGovernment (linguistics)

Abstract

fetched live from OpenAlex

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._

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.599
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.014

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.

Opus teacher head0.014
GPT teacher head0.188
Teacher spread0.174 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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