The NAFTA(ization) of Sexual Harassment: The Experience of Canada, Mexico, and the United States
Bibliographic record
Abstract
I. Cross-National Differences and "Doing Business"As international markets become increasingly more interdependent due to regional and international economic agreements, labor practices will also become more homogeneous cross-nationally.In the United States, for example, the number of U.S. female workers residing in another country has more than doubled from six percent in 1990 to twelve percent in 1995.1 It is estimated by Windham International, which conducted a survey on female expatriates, that the number of women expatriate workers will reach 20 percent (of all U.S. expatriates) by the year 2000.2The National Foreign Trade Council also estimates that approximately 225,000 Americans worked abroad in 1995, up from 125,000 in 1993.3 As businesses try to address sexual harassment issues in their domestic workforce, they must also be more conscious of working overseas with employees, customers and vendors of many different nationalities. 4 Mitsubishi Motor Manufacturing of American Inc. has learned this lesson the hard way.On April 9, 1996, the Equal Employment Opportunity Commission (EEOC) charged that female employees at the Japanese-owned Normal, Illinois automobile factory were subject to groping, sexual graffiti and abusive comments.5 Management not only failed to address complaints but actually retaliated against the women who levied charges.6 The EEOC broadened the suit to include not only charges filed in earlier private suits, but all female employees, past and present, who may have been harassed.7 It estimated that as many as 700 women may have been affected by the alleged instances of harassment.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.027 | 0.006 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".