Stimming, Improvisation, and COVID-19: (Re)negotiating Autistic Sensory Regulation During a Pandemic
Bibliographic record
Abstract
Many autistic people consider repetitive and sensory practices such as stimming central to their identity and culture. In this paper, I argue that stimming is an improvisatory practice because it constitutes an articulation of autistic aesthetics and sensory preferences, is a crucial component of autistic culture, and consists of moment-by-moment negotiations with environmental and sensory barriers. Autistic people often stim with the help of technologies such as music and stim toys or tools to mediate between inner worlds and outer environments that may over/underwhelm us. I argue that during the COVID-19 pandemic, where the objects we touch (and our bodies) have become potential locations for transmission of the virus, our relationship with stimming (and our stim tools) has changed. This article connects critical improvisation studies, discourses on autistic stimming, and affordance theory to present a framework for understanding autistic stimming during the COVID-19 era: as improvisatory responses to the opportunities and barriers presented by the pandemic. I argue that stimming during the COVID-19 era is a continuously mediated response between our body-minds and the affordances of our environment, and I maintain that this process is a lived improvisation.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.027 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".