Understanding Formal Support Networks for Persons with Intellectual and Developmental Disabilities in Illinois, USA
Bibliographic record
Abstract
Access to disability services in the United States is mediated through complex formal systems characterized by strict eligibility requirements, rigid funding mechanisms, and extensive administrative procedures. The recent landscape in the United States is characterized by a clawing back of funding for social support programs, creating even greater barriers to access for many individuals. To navigate these gaps, many families rely on natural supports such as peer networks and community based organizations. This study seeks to examine how shifting funding structures, eligibility criteria, and bureaucratic processes within formal disability services in Champaign-Urbana, Illinois influence timely and equitable access to care. Specifically, we answer the question: “How do existing funding structures and eligibility criteria in Illinois determine who receives formal disability services, and who is left out? What rationales guide these decisions in a context of limited resources?” Using semi-structured interviews with formal support providers, the research explores perceptions of systemic barriers, decision-making around support eligibility in the context of austerity, and considerations of how cuts in formal support affect natural support networks. The findings will inform policy recommendations aimed at reducing structural barriers and enhancing the effectiveness of both formal and informal systems in promoting inclusive and responsive care.
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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 | 0.001 | 0.004 |
| 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 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".