Unknowing in Practice: The Promise of Discomfort, Failure and Uncertainty in Neurodiversity Studies
Bibliographic record
Abstract
I started studying autism a decade ago as a neurotypical, non-disabled graduate student with a clear idea of what autism is. Since then, pretty much everything I thought I knew about autism, and indeed myself, has largely unravelled. A sense of lacking legitimacy in researching autism as a non-autistic academic – particularly within Critical Autism Studies with its commitment to experiential knowledge – further made for a research journey exemplified by a near-paralysing sense of discomfort, failure and uncertainty. Initially experiencing these sensations as barriers to working in the field, I gradually came to see them as productive and necessary elements of ‘unknowing’, a potential methodological and ethical approach to autism and Neurodiversity Studies. Here, I describe how I came to this way of thinking before briefly sketching out a tentative notion of what unknowing might be and do. While a few scholars have pointed to the ethical and methodological interest of forms of unknowing, none to my knowledge has suggested in simple, concrete terms some ways it might be operationalised in research. For this article, I therefore lean largely on personal experience and focus more on practice than on theory.
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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 | 0.101 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.013 | 0.188 |
| Scholarly communication | 0.030 | 0.052 |
| Open science | 0.004 | 0.027 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 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".