Animal Justice and Sexual (Ab)use: Consideration of Legal Recongition of Sentience for Animals in Canada
Bibliographic record
Abstract
This research paper looks at the judicial decisions in Canada including R v Butler, R v Labaye, and R v Sharpe to trace the court's evolving attitudes on obscenity.Specifically, this paper discusses visual arts in relation to censorship, obscenity, and pornography.The purpose of this paper is to show that the obscenity law restricts artistic freedom by requiring an unsubstantiated risk of harm.Consequently, this paper takes an anticensorship feminist approach arguing there is profound educational value to be had in allowing artists to depict morally taboo subject matter.With this in mind, this paper offers a policy recommendation to eliminate the law of obscenity and indecency.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.038 | 0.012 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 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".