Disentangling Disparate Impact and Disparate Treatment: Adapting the Canadian Approach
Bibliographic record
Abstract
Confusion. There is no better way to describe the current state of U.S. law regarding allegedly discriminatory workplace standards (e.g., height or weight requirements or drug use policies). These claims are often brought under a "disparate impact" theory of discrimination-where a facially neutral employment policy has the effect but not the intent of discriminating against a group of employees. This theory has its origins in case law rather than statutes. Indeed, it was first recognized as a viable approach by the Supreme Court in 1971. As a result, the law developed on a case-by-case basis without a solid theoretical footing, leaving many questions for judges and litigators: How does disparate impact theory interact with claims of intentional discrimination ("disparate treatment")? How are remedies awarded under the two theories? Should disparate impact or disparate treatment analysis be applied when examining an employment standard? Must disparate impact and disparate treatment be specifically pled, and does failure to do so waive a plaintiffs rights to raise these arguments? Who bears the burden of proof?. In patchworklike fashion, courts have attempted to address these issues, often with conflicting results.
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.
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 | 0.035 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.016 | 0.011 |
| Science and technology studies | 0.024 | 0.086 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.007 | 0.014 |
| Research integrity | 0.012 | 0.018 |
| Insufficient payload (model declined to judge) | 0.010 | 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".