Who needs to (un)know? On the generative possibilities of ignorance for autistic futures
Bibliographic record
Abstract
This article advances an (anti)agenda that would center unknowing as a necessary tool to remake autism. While much of the literature on the social study of ignorance describes its corrosive effects for democracy or how ignorance fuels epistemic injustice, I argue that some harms committed against autistic people have come from well-meaning attempts to know. A newly invigorated “critical” autism studies could foreground a project of ignorance to catalogue the varieties of unknowing that can recenter and remake autism. This does not entail simply supplanting “expert” knowledge with “non-expert” knowledge from the purified perspective of situated, autistic knowers; rather it disrupts feel-good narratives in which any efforts to rescue subjugated knowledges are hailed as undeniably progressive, a practice in which autistic knowers can become ensnared. Unknowing autism can propel the generative possibilities of failure, and futures in which efforts to reproduce dominant ways of knowing are resisted.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".