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Record W2669365457 · doi:10.1163/15685306-12341461

Empathic Differences in Men Who Witnessed Animal Abuse

2017· article· en· W2669365457 on OpenAlexaff
Beth Daly, L. L. Morton

Bibliographic record

VenueSociety and Animals · 2017
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsEmpathyPsychologyCognitionPerspective (graphical)Perspective-takingAnimal welfareEmpathic concernClinical psychologySocial psychologyPsychiatry

Abstract

fetched live from OpenAlex

Abstract This study draws on diverse research results from investigating the relationship between experiences with nonhuman animal abuse and empathy. We examined whether 108 men with a history of animal abuse showed differences between cognitive (perspective-taking) and affective (emotional) empathy. The effects related to three levels (never, once, multiple times) of witnessing the killing of animals and witnessing the torture of animals. Individuals who witnessed abuse were higher in cognitive empathy than affective empathy. This supports previous findings for a “dissociation hypothesis,” which suggests exposure to animal abuse may mediate between emotional and cognitive empathy. Therefore, it may be beneficial for an individual to have the ability to detach cognitive from emotional empathy—particularly those in careers related to animal welfare and veterinary care. An absence of emotional empathy may also lead to a callous or dismissive attitude to people in need. We sought an appropriate balance of the two.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.031
GPT teacher head0.338
Teacher spread0.307 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2017
Admission routes1
Has abstractyes

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