Exploring narcissism and human- and animal-centered empathy in pet owners
Bibliographic record
Abstract
Having empathy for others is typically generalized to having empathy for animals. However, empathy for humans and for animals are only weakly correlated. Thus, some individuals may have low human-centered empathy but have high animal-centered empathy. Here, we explore whether pet owners who are high in narcissism display empathy towards animals despite their low human-centered empathy. We assessed pet owners’ (N = 259) three components of trait narcissism (Agentic Extraversion, Antagonism, and Narcissistic Neuroticism), human- and animal-centered empathy, attitudes towards animals, and their pet attachment. We found that Agentic Extraversion was unrelated to both human- and animal-centered empathy. We also found that Antagonism was related to less empathy for both humans and animals, as well as more negative attitudes towards animals. Lastly, we found that Narcissistic Neuroticism was unrelated to human-centered empathy and positively related to animal-centered empathy and attitudes towards animals. This research furthers our understanding of the relation between empathy towards humans and animals and provides insight into whether animal-assisted approaches may be useful for empathy training in those with narcissistic characteristics.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".