Critiquing the Conception of “Crimes Against Nature”: The Necessity for a New “Natural” Law
Bibliographic record
Abstract
Drawing upon historical developments and legal interpretations of "crimes against nature" as it relates to bestiality, this paper endeavors to promote further discussions surrounding the conception of "natural" and/or extant law, by which an understanding of historical and modern objections to crimes against nature opens them up to the critique of thin-universalism-that is, that despite disparate socio-moral fundaments, laws of prohibition coalesce around evangelical totalities. We contend that a paradigm shift in respect of naturalness is necessitated-naturalness should be packaged and represented, in part, by the empirical. Thus, conceptions of vulnerability and sentience rooted in social scientific understandings and in hospitable forms of rights protections ought to provide a new understanding of governance of the natural. In turn, this naturalness ought to be reflected in law so long as the boundaries we propose are not unduly transgressed. If unnatural acts like bestiality have their prohibitions in the legal tethering points of Judeo-Christian and, later Victorian roots, the new natural law ought to be apprised of rights-based constitutionalism and informed by green criminological and animal rights logics. Law, apprised of these ethics, would evolve to expand protections for animals, human and non-human alike. Laws based on the new naturalness could protect species and environments as evidence demands.
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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 | 0.018 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.007 | 0.143 |
| Scholarly communication | 0.013 | 0.025 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.010 | 0.021 |
| Insufficient payload (model declined to judge) | 0.002 | 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".