Bibliographic record
Abstract
Around the turn of the millennium, there was an outpouring of autistic representation in literature, film, and television. These resulted in a multitude of new cultural texts that reinforced damaging metaphors about autism that had previously emerged in medical discourse. In film and television, autistic people are portrayed through a variety of metaphors: as impenetrable fortress, missing puzzle pieces, confusing aliens, and as malfunctioning robots or supercomputers. In this paper, I examine the role of film and television sound in reinforcing the metaphor of autistic people as “unfeeling machines.” The unfeeling machine metaphor is personified through sound tracks that deploy a number of mechanical sound effects, including vintage typewriter or calculator sounds, binary code sound effects, as well as sound mixing techniques that evoke the supposedly mechanical, and computational nature of autistic behaviour and thought processes. It is also through the autistic voice and nondiegetic music that machine metaphor are exemplified; which I argue both consciously and unconsciously influence the audience’s perception about autism. In this paper I examine films and television programs including Rain Man, Mercury Rising, The Big Bang Theory, The Good Doctor, Touch, and Atypical to reveal how the sound tracks of each film reinforces the harmful autism machine metaphor.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".