Bibliographic record
Abstract
Abstract: In this article, I examine how the spectacle of aging film stars replicates and dictates the cultural attitudes that make risking the scandal of anachronism ‘‘necessary and inevitable as a sign of life,’’ as Mary Russo puts it, even when age does not literally signify death (in other words, even when the actresses are only middle-aged) (21). With close attention to Hollywood films Sunset Boulevard and Whatever Happened to Baby Jane? I argue that the conflation of disability with gender and aging in the character of Blanche Hudson encapsulates the impossible standards imposed on all non-normative bodies (that is, all bodies) by Hollywood cinema. While aging bodies are not necessarily disabled, older people frequently face being treated as though they were physically less capable than they once were. The assumption that disability is automatic in later life has negative impacts both on those older people who do not experience significant physical impairment and on those older people who have lived with physical impairments since long before they were labelled old. In Baby Jane, the collision of Blanche Hudson’s physical disability with Jane Hudson’s psychiatric illness relies upon an understanding of their obsolescence as performers; their failed physicality mirrors their stalled career paths. However, as I will argue, the double-edged performances offered by Joan Crawford and Bette Davis undermine that obsolescence.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".