Soft Disability in Schools: Assisting or Confining At Risk Children and Youth?
Bibliographic record
Abstract
The aim in this article is to examine the institutional construction of soft or mild disability by special education as a process of pre-incarceration in schools. Soft disability includes the categories of specific learning disabilities (LD), speech and language impairments (SLI), emotional and behavioural disorders (EBD), and mild mental retardation (MMR). Based on Foucault's (1975/1977) view of the link between prison and delinquency and Skrtic's (1995) critical-pragmatic perspective of disability and special education , I argue that labels of soft disability reflect a process of suspending the educational and citizenship participation of disadvantaged students. Similarly to what Foucault described as the production of delinquency by the prison, special education, by applying inconsistent identification criteria, increases the occurrence of soft disability. Variation and inquiry are outlined as alternative concepts whose implementation may r e verse the negative school dynamic between soft dis ability and (p r e) incarceration.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| 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".