«La justice reste à venir»: la déconstruction appliquée à la décision de la Cour suprême du Canada dans l'arrêt Bou Malhab c Diffusion Métromédia CMR Inc.
Bibliographic record
Abstract
Although there have been certain advances with respect to equality rights, which are protected in the Canadian Charter of Rights and Freedoms and the Quebec Charter of the Human Rights and Freedoms, judicial decisions often ignore race as a social construct. To give full effect to the principles of equality and to advance justice, courts need to take race into account, recognize it as a social construction, and make visible what is often invisible. The technique of deconstruction, by bringing to light history and relations of power, allows us to understand social context in all of its complexity. In this quest for justice, Critical Race Theory, which recognizes the importance of a holistic approach to better understand racialization and racism provides indispensable theoretical insights. By highlighting new perspectives and providing a plurality of readings of the same event, Critical Race Theory and the technique of deconstruction promote an analysis that takes substantive equality into account while being attentive to the complex realities of multicultural society. To illustrate these themes, this thesis analyzes the Supreme Court of Canada decision on defamation and racist speech, Bou Malhab v. Diffusion Métromédia CMR Inc., arguing that the Court was not sufficiently attentive to the effects of race as a social construct, nor to the realities of racism.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.021 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".