Promoção e avaliação da percepção de alunos e servidores públicos sobre a relação existente entre os maus-tratos aos animais e violência doméstica
Bibliographic record
Abstract
Currently we have tried to relate crimes of domestic violence with cruelty animals. This action is already performing in the USA and Canada,,however in Brazil the issue is not widely discussed, making important the position of educational institutions and veterinarians with proactive attitudes in the context of Shelter Medicine, performing an intersectoral work and involving different levels of government. The relation that mistreatment and domestic violence are kinds of abuses that have connection was class topic in the discipline of Ethics and Legal Veterinary Medicine in the course of Veterinary Medicine at Instituto Federal de Educação, Ciência e Tecnologia do Norte de Minas Gerais – Campus Salinas, expanded the classroom boundaries and acquiered extensionist nature to the Campus’s community, generating this work, which aimed to evaluate and promote a perception of students and staff of INFMG - Campus Salinas about a relationship between animal abuse and domestic violence. The activity proved to be of excellent professional improvement for students at the same time revealed that the subject is extremely new in the local community and may serve other actions in the teaching, research and , extensionista activities, base of the ,Higher Education Instituitions in the country, which can initiate intersectoral work between the secretariats of its municipalities and/or regions.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".