Employment Inclusion and Social Sustainability for Individuals with Intellectual Disabilities
Bibliographic record
Abstract
Employment inclusion for individuals with intellectual disabilities remains a critical challenge despite decades of policy reform and programmatic innovation. This paper examines the intersection of employment models, social sustainability frameworks, and systemic barriers affecting competitive integrated employment outcomes for this population. Through systematic analysis of empirical evidence and policy literature, the study evaluates supported employment, customized employment, sheltered workshops, and competitive integrated employment models, identifying multilevel barriers, attitudinal, systemic, and employer-side, that constrain labor market participation. Findings indicate that supported and customized employment approaches significantly increase competitive integrated employment likelihood when paired with individualized job coaching, natural supports, and employer capacity building. However, national employment rates remain persistently low, reflecting fragmented service systems and inadequate interagency collaboration. The paper synthesizes evidence on effective workplace supports, reasonable accommodations, and transition planning while highlighting the role of U.S. legislative frameworks, particularly the Workforce Innovation and Opportunity Act, in advancing employment-first policies. Recommendations emphasize holistic system reform, standardized evaluation practices for social enterprises, and equity-centered approaches that address intersectional barriers. This analysis contributes to understanding how social sustainability principles, balancing human resource supports with organizational viability, can inform durable, rights-based employment pathways for individuals with intellectual disabilities in the United States.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.012 |
| 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".