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Roundtable Review: Guilty Pleasures

2021· article· en· W4236798713 on OpenAlexaffabout
Arielle Zibrak, Sarah Danielle Allison, Rita J. Dashwood, Melissa Gniadek

Bibliographic record

VenueEdith Wharton Review · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicGothic Literature and Media Analysis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsScholarshipConversationRomancePleasureSociologyStyle (visual arts)Media studiesReading (process)ArtLiteraturePsychologyLawPolitical science

Abstract

fetched live from OpenAlex

This roundtable review of Arielle Zibrak's book Guilty Pleasures inaugurates what we hope to be a more dynamic, interactive format of exchange that works in tandem with traditional book reviews. In a (still not quite post–) COVID-19 pandemic world, new forms of cultural discourse have emerged, breaking down long-held, albeit already fluid, boundaries between public and private spaces; collective and individual identities; conventional and newly minted modes of interaction, communication, and scholarship. Guilty Pleasures brilliantly thematizes and embodies the need to revise the rigid boundaries of scholarly conversation. Hailing from the author's reflection on her own culture of reading femme fictions as a rite of passage into a world of “guilty pleasures”—such as romance novels, romantic comedies, and popular, female-centered television shows—Guilty Pleasures deftly weaves the nineteenth century with gender studies, cultural critique, and affect theory. With a candid, conversational style, the book bridges academic and popular writing in a way that engages the reader to do the same, broaching such important questions as the nature of pleasure, the experience of guilt, and structures of love, sex, and gender.In what follows, Sarah Danielle Allison (Loyola University), Rita Dashwood (Edge Hill University), and Melissa Gniadek (University of Toronto) join author Arielle Zibrak (University of Wyoming) in an incisive discussion of Guilty Pleasures.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.028
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.037
GPT teacher head0.363
Teacher spread0.326 · 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; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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
Published2021
Admission routes2
Has abstractyes

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