Protecting the Wolf in Sheep's Clothing: Perverse Consequences of the McKennon Rule
Bibliographic record
Abstract
Suppose an employer accused of discrimination learns during litigation that the plaintiff had fabricated a college degree, hidden a criminal conviction, or stolen confidential documents. Under the landmark case of McKennon v. Nashville Banner Publishing Co., the U.S. Supreme Court determined that such plaintiffs might obtain relief even if the after-acquired evidence would have led to lawful termination or lawful failure to hire if defendants had uncovered it when it occurred. Most commentators applaud McKennon but fail to acknowledge its perverse consequences in diluting the effectiveness of labor-market signaling. The unintended result may be reduced productivity, poorer matches of workers to jobs, higher turnover, and increased costs of hiring and assessment. The ruling may also increase the incidence of statistical discrimination as well as erode the benefits of acquiring human capital. In sum, despite its praiseworthy objectives, McKennon may worsen labor-market conditions for those protected by discrimination laws. This paper describes the typical scenarios that arise in after-acquired-evidence cases, reviews the law surrounding McKennon, and discusses how the economic literature on information and signaling applies to such cases.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".