Animals as Something More Than Mere Property: Interweaving Green Criminology and Law
Bibliographic record
Abstract
Our article argues that non-human animals deserve to be treated as something more than property to be abused, exploited, or expended. Such an examination lies at the heart of green criminology and law—an intersection of which we consider more thoroughly. Drawing upon our respective and collective works, we endeavor to engage in a discussion that highlights the significance of green criminology for law and suggests how law can provide opportunities to further green criminological inquiry. How the law is acutely relevant for constituting the animal goes hand in glove with how humanness and animality are embedded deeply in the construction of law and society. We contend that, when paired together, green criminology and law have the potential to reconstitute the animal as something more than mere property within law, shed light on the anthropocentric logics at play within the criminal justice system, and promote positive changes to animal cruelty legislation. Scholarship could benefit greatly from moving into new lines of inquiry that emphasize “more-than-human legalities”. Such inquiry has the power to promote the advocacy-oriented scholarship of animal rights and species justice.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.114 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| 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".