Animal Cruelty: Pathway to Violence Against People. By Linda Merz-Perez and Kathleen M. Heide (Walnut Creek, CA: Alta Mira Press, 2004, £21.00 pb)
Bibliographic record
Abstract
In Animal Cruelty: Pathway to Violence Against People, Linda Merz-Perez and Kathleen M. Heide provide insight into the horrors of violence against animals by building on previous research that examined the link between animal cruelty and human interpersonal violence. The study begins with four case studies of animal cruelty and the authors appropriately warn that these vignettes are not to be read by the squeamish. These anonymous cases of human violence against animals would be an affront to any decent sensibility. They demonstrate the extremes that outright cruelty, abuse and/or neglect can manifest. For those readers unaccustomed to the severe outcomes of violence towards animals, these cases provide a crash course on its manifestations and the outcomes of such acts, for both the animal victims and the animal care providers. The authors provide a good overview of the animal cruelty literature. This includes studies that examine the general literature on interpersonal violence as well as those that focus on the specific issues of human cruelty towards animals. Thus, the literature review covers research that examine the ‘triad’, i.e. bedwetting, fire setting, and torturing small animals; literature that explores animal cruelty as a precursor to human violence; studies that address animal cruelty as part of an antisocial continuum that may pre-date or post-date violence against humans; and work which advocates that cruelty against companion animals is a form of family violence. These sections alone would make the purchase of this text worthwhile, especially for a novice in this area hoping to quickly develop a proficiency in the field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.176 | 0.068 |
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".