The Importance of Studying Global Issues in Employment Discrimination Law
Bibliographic record
Abstract
Recently, General Motors of Canada fired 172 workers because they held dual citizenship C Canadian and other countries.The firings were prompted by United States Department of State rules governing who can work on U.S. military projects.As reported by the New York Times, Anext month, General Motors will face an Ontario Human Rights Tribunal hearing over some of the London plant layoffs.That is a reminder for G.M. that to meet State Department rules, foreign-based military contractors often have to break, or at least challenge, local human rights and employment laws.@The story quotes a spokeswoman for Canada=s Department of National Defense: ACanada regards discrimination against workers based on citizenship or country of origin as a violation of the country=s charter of rights and freedoms.….Ms. Hodges said Canada=s defense department would never discriminate against a worker to meet the United States rules.@Ian Austin, Strict
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.024 |
| Scholarly communication | 0.012 | 0.021 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 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".