THE TURN OF THE “BAD FEMINIST”: PROBING MONSTROSITY IN THE SHARED UNIVERSE OF THE HANDMAID’S TALE
Bibliographic record
Abstract
This essay aims at tentatively probing the figure of the “bad feminist” in the shared universe of The Handmaid’s Tale, composed as it is by Hulu’s adaptation of Margaret Atwood’s homonymous 1985 novel and the Canadian author’s 2019 sequel, The Testaments. After briefly examining the figure of the “bad feminist” in the context of the fourth wave of feminism, we offer notes on how the characters of June Osborne in Hulu’s series and Aunt Lydia in The Testaments may have been rendered monstrous bad feminists for their rejection of norms of solidarity, a constitutive and dominant tenet of fourth-wave feminism, seeing how the monster could be described as the embodiment of the anti-norm which renders normatvie social configurations visible and stable.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.018 | 0.063 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".