Disturbing Behaviours: Ole Ivar Lovaas and the Queer History of Autism Science
Bibliographic record
Abstract
This paper “queers” the history of autism science through an examination of the overlap between the regulation of autism with that of gender and sexuality in the work of Ole Ivar Lovaas. Lovaas is the founder of Applied Behaviour Analysis (ABA), the most commonly used and funded autism intervention today that seeks to extinguish autistic behaviors, primarily among children. Less commonly recognized is Lovaas’ involvement in the Feminine Boy Project, where he developed interventions into the gender identities and behaviors of young people. Turning to Lovaas’ published works, we perform a “history of the present” and argue that a queer disability studies lens opens up the richness of autism as a cultural nexus, and deepens understandings of intersecting and contested histories of science, professional scopes of practice, and dominant futurities. The article makes a significant and timely contribution to understanding the disabling material effects of autism science in the lives of autistic persons. In particular, this case study highlights the need for feminist science studies to further investigate the historical and contemporary links between dominant scientific constructions of disability, gender, and sexuality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.046 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".