Semiotic Stalemate: Resisting Restraint and Seclusion through Guattari’s Micropolitics of Desire
Bibliographic record
Abstract
This article explores the semiotic relationships between Applied Behavior Analysis research, special education practice and restraint and seclusion policy by tracing the evolution of the concept of “self-restraint,” —a term from Behavior Analytic literature for a variety of “behaviors” in which a person restricts their own movement. I trace how “self-restraint” emerges as a new class of behaviors eligible for intervention, and how this marks certain bodies for restrictive practices such as restraint, seclusion and the use of aversives. I explore how rhetorical moves shape the educational landscape of disabled students and expose mechanisms of control that are shaped by scholarship. By using “self-restraint” as an example, I respond to the taxonomies of deficit disseminated through Applied Behavioral Analysis in schooling for neurodivergent students and make critical links between special education practice and Disability Studies in Education.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.075 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| 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".