White coat, white alb, white mic: Institutions of truth in America in American Horror Story: Asylum
Bibliographic record
Abstract
Abstract When Lana Winters of American Horror Story: Asylum releases footage of inhumane conditions at Briarcliff, she does so without the consent of those depicted. Her reliance on the suffering at Briarcliff to forward her own goals echoes two other key figures in Asylum: Dr Arden and Monsignor Howard. Arden’s advocacy of medical science and its potential to save humanity seem in sharp contrast to his horrific experimentation on what he sees as bare, wasted life. His position as fictional character with past experience as a Nazi doctor grounds his actions in a nexus of hyperreality and banality. Monsignor Howard believes himself a paragon of the church, whose obsessive ambitions towards Rome contrast his murderous, lustful and corrupt behaviours. He is not only complicit in Arden’s experimentation, but he takes life himself; yet, as he moves to New York as Cardinal, he looks past those abominations to focus on the greater good of the institution. In an echo of that wilful blindness, Lana is capable of looking away from the suffering of the very people she takes as an object. What makes Lana ever so slightly different from the others is her movement through the category of bare life (Agamben 1995: various) and her subsequent refusal of a ‘futurism that’s always purchased at [the] expense’ of the marginal (Edelman 2005: 4). The social institutions that the characters operate within allow and encourage their sociopathy (Asma 2009: 244–45). The danger of the institution is its capacity for creating and concealing monstrosity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.036 | 0.022 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".