Compartmentalization of Animals: Toward an Understanding of How We Create Cognitive Distinctions between Animals and Their Implications
Bibliographic record
Abstract
Abstract The nature of our relationships with nonhuman animals is complex and varies greatly across different types and species of animals. The goal of the current research is to investigate the differences that exist in our perceptions of animals based on their type, specifically by focusing on the phenomenon of compartmentalization. Two studies investigated the compartmentalization of farm animals relative to other types of animals (e.g., pets, wild animals). In Study 1, a greater tendency to compartmentalize farm animals correlated negatively with the attribution of a higher status to these animals, with more differentiated perceptions between the standing of farm animals and pets, and with a lower inclusion of animals in the self. In Study 2, different justifying beliefs taping into human superiority, the endorsement of carnism, and feeling threatened by vegetarianism mediated the negative relation between compartmentalization of farm animals and the negative emotional outcomes felt when eating meat. Together, these findings confirm the relevance of applying the notion of compartmentalization to the specific realm of human–animal relations.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.030 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".