The Limitations of Disability Antidiscrimination Legislation: Policymaking and the Economic Well‐being of People with Disabilities
Bibliographic record
Abstract
Although Congress passed the A mericans with D isabilities A ct ( ADA ) to address, in large part, the declining economic well‐being of people with disabilities—twenty years later—the trend has not reversed. To shed light on this puzzle, we use multilevel models to analyze Current Population Survey data from 1988 through 2012 matched with state‐level predictors. We take a more nuanced approach than previous research and consider institutional factors related to the creation, enforcement, and interpretation of legislation, as well as individual demographics and employment situations. Our results show continual gaps in employment and earnings by disability status connected to the enactment of state‐level antidiscrimination legislation, the number of ADA charges brought to the Equal Employment Opportunity Commission, and the results of ADA court settlements and decisions. Our findings suggest a complex relationship between legislative intent and policy outcomes, showcasing the multilayered institutional aspects behind the implementation of disability antidiscrimination legislation.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".