When ‘atypical’ is the new typical: a critical analysis of the representation of virginity among neurodivergent men characters in TV series
Bibliographic record
Abstract
This article critically reflects on a television trend that remains poorly documented, namely that of representing male virginity as being caused by a form of neurodivergence. As part of an interdisciplinary research project on sexually inexperienced emerging adults (SIEA) in North American films and TV series, a textual analysis has been conducted for four TV series portraying sexually inexperienced men on the spectrum (The Good Doctor, Atypical, The Big Bang Theory, L’Heure bleue). Our research shows that this tendency to associate men’s sexual inexperience with autism is an important issue that reveals the persistence of gender and ableist norms in TV series. For instance, TV series contribute to the reproduction of the stereotype that a man on the spectrum is de facto sexually inexperienced, and then prioritize a “virginity loss” narrative. Characters also acquire maturity after their first sexual encounter, resulting in increased masculine capital. Considering that such series seek to represent neurodiversity, the lack of diversity when it comes to representing sexual inexperience is questioning. The fact that narratives invariably allow neurodivergent men to gain sexual experience seems to reinforce the cultural norm that sexual activity is mandatory, especially for adult men.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".