Policing Farm Animal Welfare in Federated Nations: The Problem of Dual Federalism in Canada and the USA
Bibliographic record
Abstract
In recent European animal welfare statutes, human actions injurious to animals are new "offences" articulated as an injury to societal norms in addition to property damage. A crime is foremost a violation of a community moral standard. Violating a societal norm puts society out of balance and justice is served when that balance is returned. Criminal law normally requires the presence of mens rea, or evil intent, a particular state of mind; however, dereliction of duties towards animals (or children) is usually described as being of varying levels of negligence but, rarely can be so egregious that it constitutes criminal societal injury. In instrumental justice, the "public goods" delivered by criminal law are commonly classified as retribution, incapacitation and general deterrence. Prevention is a small, if present, outcome of criminal justice. Quazi-criminal law intends to establish certain expected (moral) standards of human behavior where by statute, the obligations of one party to another are clearly articulated as strict liability. Although largely moral in nature, this class of laws focuses on achieving compliance, thereby resulting in prevention. For example, protecting the environment from degradation is a benefit to society; punishing non-compliance, as is the application of criminal law, will not prevent the injury. This paper will provide evidence that the integrated meat complex of Canada and the USA is not in a good position to make changes to implement a credible farm animal protection system.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".