Bibliographic record
Abstract
This article explores what legal scholars might learn from the neo-classical Law and Economics tradition in order to more effectively promote the equality rights of workers with disabilities. A long marginalized group that has to date received relatively little attention from Canadian legal scholars, people with disabilities experience systemic barriers in many aspects of society including in employment, education and transportation. The social model of disablement has identified structural barriers in society as the primary issue responsible for the marginalization of people with disabilities. However, there has been to date little engagement in Canadian disability rights scholarship with the Law and Economics tradition and this article attempts an initial and tentative overview. Popularized by scholars as Richard Posner, the Law and Economics tradition has become increasingly influential and seeks to use the tools offered by neo-classical economics to analyze the efficiency of legal rules as rational actors seek to maximize their utility. Reflecting insights from both the Canadian and American experience, this article proposes to critique the tools of neo-classical economics to discern what elements might prove to be useful in empowering people with disabilities in the workplace. Through a critical appraisal of Law and Economics concepts such as cost-benefit analysis, statistical discrimination and the traditional neo-classical notion of unions as monopolies, some parameters are set for further inquiry.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.059 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| 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".