Sibling Relationships and Developmental Disability Services: From Coerced Care to Entitlement
Bibliographic record
Abstract
This article critically assesses the systems that structure unpaid care work for people with intellectual disabilities, with a focus on the role of siblings. We provide a preliminary analysis of this current trend in unpaid care work in the province of Ontario, Canada, addressing practices that are a) built upon a devaluation of people with intellectual disabilities, and that b) deny them choice in who provides them care. We combine existing evidence with relevant survey data to assess the risks associated with what we characterize as coercive care, as well as the many tensions that arise between self-advocacy and family-led advocacy initiatives. We interrogate the assumption that the role of siblings, and women in particular, is to take over unpaid care roles from parents. We also suggest how the current socioeconomic context of many individuals and families can limit opportunities for adopting potential solutions and propose practical avenues for future research. Throughout our analysis, we centre questions of agency and self-direction, pointing to the clash of values and inequitable outcomes that makes dominant support arrangements untenable. We conclude by drawing an ideal scenario of the publicly funded supports and services to which people with intellectual disabilities should be entitled and outline the many implications attached to this proposed model.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".