Retaliation: Why an Increase in Claims Does Not Mean the Sky Is Falling
Bibliographic record
Abstract
Retaliation, the fastest growing cause of action in discrimination law, has gained considerable attention, following two cases decided by the U.S. Supreme Court. The 2006 Supreme Court case, White v. Burlington Northern, and the 2009 Supreme Court decision in Crawford v. Metropolitan Government of Nashville has made retaliation front-page news. White created legal ambiguities that allow plaintiffs? lawyers to posture and leave management lawyers without the tools to adequately render legal advice. Crawford expanded the opposition clause, which involves an employee?s resistance to a perceived violation of discrimination statutes. While there?s no indication that either case should result in more plaintiff victories (nor prevent employers from managing their workforce), the cases will, in all likelihood, lead to a further increase in claims. They also could change some management practices, despite causing no major changes in plaintiffs? abilities to succeed in their claims. Following extensive discussion at the 2008 and 2009 Labor and Employment Law Roundtables, we conducted this analysis to examine the effects of the White and Crawford cases and explain the law surrounding retaliation. Before we address these issues, however, we examine the statistical increase in retaliation claims and hypothesize why these cases are on the rise.
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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 | 0.004 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".