A Classical Perspective on the Modern Workplace: The Aristotelian Conflict in Sexual Harassment Litigation
Bibliographic record
Abstract
The current structure of sexual harassment litigation in Canada poses unique issues for Aristotle’s distinction between corrective and distributive justice. Due to a series of decisions in the 1980s and 1990s, sexual harassment claims in Canada must be brought exclusively under human rights legislation. This system views sexual harassment as a form of sex discrimination, and roughly subscribes to the view that sexual harassment is an incident of distributive injustice. However, the form of the litigation tends to undermine its distributive justifications. The litigation generally corresponds to the traditional adversarial model, with the complainant seeking damages from the harasser. Consequently, some commentators have argued that sexual harassment should be treated as a private law cause of action, rather than an issue for the human rights system. This article examines the rationale for classifying sexual harassment as an object of corrective or distributive justice, and particularly whether harassment should be viewed as an individual or a group harm. It also addresses the emerging claims for heterosexual male-on-male or bisexual harassment, which create problems for the view that sexual harassment is a form of discrimination “because of sex.” Finally, the article explains how the distributive and corrective theories of justice are manifested in the extent of employer liability and the available remedies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.013 | 0.061 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".