Legal Criticism of The Conceptualization of The Legal Good Protected Against the Crime of Animal Abuse in Peru
Bibliographic record
Abstract
Animal abuse is understood as non-accidental and socially unacceptable human behavior that causes pain, suffering or distress and/or death to an animal. This article aims to determine the conceptualization of the legal good protected against the crime of animal abuse when they are improperly exploited or treated with extreme cruelty; in the face of the impact on biodiversity and the ecosystem, typified in the Peruvian criminal system within property crimes, which leads to the interest of the owner being protected and not the life of the animal, producing legal-criminal conflicts in the sense that a difficulty is created to punish the crime. The method used is a topic review, exploratory in scope, with a thematic analysis design. From the results it has been corroborated that the annual number of animal abuse in the world amounts to 115 million, only to use them for cosmetic and scientific purposes; Japan, the United Kingdom, China, Canada and the United States are the countries that together carry out this practice in a percentage greater than 55% of the 115 million. It is concluded that Peruvian Law No. 30407 classifies animal abuse as a crime, in article 206-A of the Penal Code, producing doctrinal discussions referring to the protected legal good, causing insufficient guarantees to protect the animal life that society and society Actually, it demands it.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.034 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".