Bibliographic record
Abstract
This article questions the ways autism knowledge is racially assembled. Of specific interest is how clinical and cultural definitions of autism routinely deny the existence of autistics of colour and regularly instantiate autism as a White condition. Employing a contrapuntal reading of autism knowledge, which foregrounds the life-writings of autistics of colour, this article argues that disproportionality and delayed autism diagnoses for children of colour as well as autistic Whiteness habituates autism’s diagnostic space. Not only does this result in the clinical and cultural exclusion of children of colour from autism knowledge, it also hierarchically orders humanity. While autism has received recent philosophical attention from Ian Hacking, this article suggests that Hacking’s historical ontology does not adequately attend to the racializing effects of autism knowledge. As such, this article concludes by gesturing toward the need to re-assemble autism’s diagnostic shape through the invention of collective sites of expression which make possible #BlackAutisticJoy.
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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.013 | 0.104 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.001 | 0.012 |
| 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".