Bibliographic record
Abstract
In season 2, episode 4 of the critically acclaimed British television series Fleabag, the eponymous main character speaks to her best friend, Boo, in a flashback following Fleabag’s mother’s funeral. “I don’t know what to do with it,” Fleabag tells Boo. “All the love I have for her.” Here, Fleabag demonstrates what I refer to as affective excess—an overwhelming emotional response that feels too much for an individual to bear alone. In this case, Fleabag feels too much love for her mother but has no mother to receive it. Boo responds in her typical, guileless way: “I’ll take it. No, I’m serious. It sounds lovely.” The series implies that Fleabag channeled her love for her mother, and the grief accompanying her death, into her relationship with Boo. However, after Boo also dies, the viewer is introduced to one of the show’s central questions: Where can one place their affective excess when the usual channels are no longer available? The show explores how the intersecting demands on Fleabag—as both a feminist and a neoliberal postfeminist agent—leave her without an adequate outlet for unburdening her excess grief and guilt, ultimately rendering healing and relief impossible. Despite her avowed atheism, Fleabag appears to eventually find relief through spiritual means, specifically confession. This article argues that while Fleabag offers a powerful feminist critique of neoliberal society, it also presents an implicit reflection of the limits of feminism and advocates for a transcendent element in human relationships as the only adequate place to unburden affective excess.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.019 | 0.007 |
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".