Bibliographic record
Abstract
Under the radar Lifetime dramedy Drop Dead Diva (2009–2014) demonstrates a conflicting message that both feminism (understood as careerism) and femininity (beauty-obsessed ‘diva’) hold women back. The ‘living fat suit’/body switch between a model (Deb) and an unfeminine plus-sized lawyer (Jane) attempts to resolve these tensions by teaching the model about fat loathing while feminizing an overly careerist feminist. The show departs from previous body-swapping comedies; the ‘swap’ is permanent, the new Deb/Jane retains legal knowledge and abilities from the body of Jane, and the remaining body (Deb) and soul (Jane) both die for good. The show is interesting not because it has a size 16 lead but because of how a size 16 lead can be incorporated into an already rigid gender and sexual bodily normativity by disciplining an unruly fat woman’s ‘lesser’ desires, and slenderizing her appearance, bringing her into a white heterosexual visual gender economy. Drawing on my failure to be moved in the right way by the ‘humourous’ premise, I outline how on the surface the show rehearses feminist arguments for bodily acceptance but ultimately promotes an aversion to feminism, trivializes femininity and straightens queerness, collapsing any fat politics into an individualized and apolitical ‘anti-looksist’ message.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.009 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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".