After Labaye: The Harm Test of Obscenity, the New Judicial Vacuum and the Relevance of Familiar Voices
Bibliographic record
Abstract
In R. v. Labaye, the Supreme Court of Canada finally retired the community standards of tolerance test of obscenity. The test had been the subject of much academic critique, a matter that reached its zenith in the period following Little Sisters Book and Art Emporium v. Canada (Minister of Justice), in which a gay and lesbian bookshop contested the procedures and legislative regime of customs officials in detaining its imports. The engagement in the literature on the efficacy of the community standards test that followed was often heated, always interesting, and ultimately unresolved. To date, we have not seen any clarifying applications of the newly proposed harm test by the Supreme Court, nor have we seen a profound articulation in any lower courts. Subsequently, the academic discussion has slowed to a crawl. In this article, the author reviews four accounts of the community standards test that were prominent following Little Sisters, and asks if the newly proposed Labaye standard meets their concerns. The Labaye case provides much fodder for the previous critics and supporters of a community standards of tolerance approach to analyze. After a critical analysis of the new Labaye test, the author concludes that the concerns have not been muted by the retirement of the community standards test, even if the voices have been. The engaged voices heard in the aftermath of Little Sisters should not hold back and they should not abandon the work to be done in obscenity law and freedom of expression discourse generally.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.041 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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".