Civil Law Alternatives in the Fight Against Hate Speech: The Case Study of the Marcus Hyman Act
Bibliographic record
Abstract
The difficult question of how to hold hatemongers legally accountable has lately taken on greater urgency as hate speech has risen to disturbing levels. In the fight against harmful speech, Canada is heavily reliant on the criminal law. An alternative legal remedy might be provided through civil actions for group defamation. Such lawsuits are currently permitted only in Manitoba. The Manitoba legislation was enacted in the 1930s through the efforts of Jewish MLA Marcus Hyman as a means for combating the fascist Canadian Nationalist Party headed by William Whittaker. This article provides a history of the Marcus Hyman Act. The Hyman Act has been discussed in several works but none of these sources offers a comprehensive analysis of the legislation’s origins and history. This article fills this gap. While historians have suggested that the Hyman Act—and its subsequent invocation in a lawsuit against Whittaker brought by William Tobias—was inconsequential, in fact it represented an important victory against antisemitism in the 1930s. Although it has fallen into disuse, the legislation’s history indicates that it could provide a useful mechanism for countering racism in the present day.L’enjeu difficile concernant les moyens pour tenir les semeurs de haine légalement responsables a récemment pris une importance accrue, car les discours haineux ont atteint des niveaux inquiétants. Dans la lutte contre ces types de discours, le Canada s’appuie fortement sur le droit pénal. Un autre recours juridique pourrait être fourni par des actions civiles pour diffamation collective, de telles poursuites n’étant actuellement autorisées qu’au Manitoba. La législation manitobaine a été promulguée dans les années 1930 grâce aux efforts du député juif Marcus Hyman comme moyen de lutter contre le parti nationaliste canadien fasciste dirigé par William Whittaker. Cet article présente l’historique de la Loi Marcus Hyman. La loi Hyman a été abordée dans plusieurs ouvrages, mais aucune de ces sources ne propose une analyse complète des origines et de l’histoire de la législation. Cet article comble cette lacune. Alors que les historiens ont suggéré que la loi Hyman — et son invocation ultérieure dans un procès contre Whittaker intenté par William Tobias — était sans conséquence, elle a en fait représenté une victoire importante contre l’antisémitisme dans les années 1930. Bien qu’elle soit tombée en désuétude, l’histoire de cette législation indique qu’elle pourrait constituer un mécanisme utile pour contrer le racisme de nos jours.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".