Commentary on Agreement between Harvey Weinstein and The Weinstein Company Holdings LLC, as of October 20, 2015
Bibliographic record
Abstract
The #MeToo movement exploded in the wake of Harvey Weinstein’s sexual misconduct while being a director and executive for TWC. Over 100 women have reported abuse or harassment by Weinstein in the course of his employment. Employment Agreements are given great leniency to draft around default rules in many states. As Professor Alexandra Andov describes in her commentary, the terms of Weinstein’s various employment agreements incentivized or sheltered behavior and crimes that harm women. Limited oversight and reporting, expansive indemnification provisions, underinclusive codes of conduct, and overly protective terms of termination contributed to Weinstein’s reign of terror on women around him. Professor Susan Chesler provides a modified employment contract that features narrative theory and “tone from the top” to increase accountability for employees and protection for victims. Drafting employment contracts to achieve these goals is achieved by mandated reporting of all incidents, eliminating indemnification to the wrongdoer for sexual wrongdoing, requiring compliance with investigations, and subjecting termination to cause employees to terminate without the ability to cure instances of sexual misconduct. Chesler’s contract is a starting point for transforming relationships across an entire organization, provide voice for stakeholders, and foster a culture that respects women’s dignity.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.022 | 0.025 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".