The Failures and Promise of the Workforce Innovation and Opportunity Act
Bibliographic record
Abstract
Abstract In the next decade, over 500,000 students with autism spectrum disorder will graduate high school, over 60% with average to above-average IQs (Institutional Center for Special Education Research, 2011). Attention is rightfully drawn to the potent challenge of optimizing lifespan outcomes for Generation A. The Workforce Innovation and Opportunity Act (WIOA) passed in 2014 calls for a unified and social model supports structure to help the education to pre-employment transition through “Required Activities.” This includes job exploration counseling, integrated work-based learning experiences, postsecondary educational programs at institutions of higher education, social skills, and self-advocacy training. WIOA aims to streamline Pre-Employment Transition Services and end the medical model, deficits-based approach from education to integrated, paid employment. The authors of the bill realized the necessity to achieve its goals through “Authorized Activities” encompassing the implementation of effective strategies for integrated work and independent living, the dissemination of information and knowledge across multistate partnerships, and learning new skills to support students in vocational rehabilitation (VR) and educational settings. The 2019 interpretation of WIOA states the educational and VR systems cannot draw funding from “Authorized Activities” and must instead focus on “Required Activities” leaving a gaping hole in the provision of services through lack of training and partnerships. Despite billions in government funding, systems remain siloed. Over 50% of autistic adults remain in segregated, subminimum wage jobs, and the 85% underemployment or unemployment rate for autistic graduates, with and without college degrees, remains. Generation A calls for the effective delivery of WIOA to enjoy integrated, meaningful employment and financial independence.
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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 | 0.046 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.019 | 0.023 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".