Productivity-Based Wages and Employment of People With Disabilities: International Usage and Policy Considerations
Bibliographic record
Abstract
The legal requirement for employers to compensate workers at standard market wages, even if their work falls below competitive levels, is cited as a barrier to job entry for people with high support needs. Productivity-based wage systems have been implemented in some jurisdictions with a goal of addressing this challenge by providing an option for paying workers at rates commensurate with work output. This scoping review explored the international use of productivity-based wage systems, the theoretical and practical arguments that have been advanced for and against productivity-based wage systems, and the relative impact of such policies on employment outcomes. The review followed the procedures outlined by Arksey and O’Malley and included papers published from 2008 to 2017. The search identified 27 papers that were pertinent to at least one of the research questions. Only three countries emerged in the literature as having discernable productivity-based wage policies: Australia, Israel, and the United States. Limited evaluative evidence was identified on the impact of productivity-based wage systems on employment outcomes. There is, however, a robust debate evident concerning the socioeconomic, moral, and legal implications of this practice. Ongoing research is needed to inform policy on this contentious issue.
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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 | 0.043 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.021 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".