Roundtable Review: Guilty Pleasures
Bibliographic record
Abstract
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.
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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 | 0.011 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.053 | 0.031 |
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".