Civil Rights in the Workplace: It's Time to Cut the Excess and Get to the Truth
Bibliographic record
Abstract
On February 8, 1964, during the last few hours before the enactment of the Civil Rights Act, thanks to an individual described as a “racist, male octogenarian,” Congress haphazardly added “sex” as a prohibited basis for discrimination alongside race, color, religion, and national origin under Title VII1 of the Act. It was not until two decades later, in 1986, that the Supreme Court recognized sexual harassment as a form of sex discrimination under Title VII. Five years later, Anita Hill’s riveting public testimony, during the nomination hearings for Justice Clarence Thomas, ignited a nationwide discussion on sexual harassment. A quarter century later, a celebrity tweet in 2017 asking for “me too” sexual harassment stories reignited the conversation and reminded us that sexual harassment was not only an active issue but a hugely pervasive problem in the American culture and workplace.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.034 |
| Scholarly communication | 0.020 | 0.023 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.015 | 0.033 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".