Animals & Section 7: How Early Charter Jurisprudence Supports Protections for Animals
Bibliographic record
Abstract
In Canadian law, animals hold an interesting legal status. On a metaphorical spectrum from property to personhood, some consider animals to be (1) pure property, (2)somewhere in the middle of the spectrum, or (3) a little bit of both property and person.The leap to full personhood is regarded as highly aspirational and not realistically viable at the present time. Despite this, advocates continue to develop novel legal arguments which shift animals closer to achieving full legal personhood, and the benefits which stem therefrom. This paper adds a novel – and admittedly highly aspirational – approach to animal personhood: entitlement to protections under section 7 of the Canadian Charter of Rights and Freedoms. Early Supreme Court jurisprudence defining ‘everyone’ within section 7 is explored. Although the conclusion of the Court states that only humans are deserving of section 7 protections, the ratio behind that conclusion leaves room for an argument for nonhuman animals to be included. Using the language of the Court, I argue that section 7 not only protects the human experience, but protects the sentient experience. Therefore, all sentient creatures are deserving of the protection of life, liberty, and security of the person.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.041 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".