Employment Inclusion and Social Sustainability for Individuals with Intellectual Disabilities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it