Doing it differently: emancipatory autism studies within a neurodiverse academic space
Bibliographic record
Abstract
In the current research climate, in which many autistic and autism communities are increasingly calling for a move towards collaborative forms of research, we consider how a loosely formed epistemological community may serve to challenge ‘business as usual’ in the academy. Mindful of the need to move beyond theory, we use this experience to concretely consider how knowledge about autism and neurotypicality can be meaningfully (co)-produced, and made available both to the research community and also to autistic and autism communities. Here, we use our own co-production of this article to explore how autistic experience may trouble normative meanings of academic knowledge production. We also consider the limits and possibilities of a neurodiverse research collaboration to reflect on ways in which a loose epistemological space may serve to contribute to knowledge about both autism and neurotypicality, adding to debate around collaborative research.
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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 | 0.032 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.025 | 0.078 |
| Scholarly communication | 0.021 | 0.020 |
| Open science | 0.002 | 0.043 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".