Bibliographic record
Abstract
Empathy is defined as the ability to share feelings and emotions of another individual (Daly & Morton, 2009). A person’s level of empathy can be affected by many factors, such as exposure to childhood trauma (Greenberg et al., 2018), mindfulness training (Birnie et al., 2010), and childhood pet ownership (Daly & Morton, 2006; Vidovic et al., 1999). It is the last of these items that is of interest to the present study. Specifically, it is unclear whether one’s change in empathy is due to pet ownership itself, or the result of changes in other personal factors that tend to coincide with pet ownership. The present study is therefore designed to expand on our understanding of why pet ownership is associated with empathy change, and will assess the relationship between empathy and one’s relationship with animals, the bond an individual has with their pet, and or one’s level of self-awareness (the ability to distinguish oneself and one’s values from others, Froming et al., 1998), among others. Therefore, we predict that in general, owning a pet will be associated with higher levels of human-centered and animal-centered empathy. However, we also predict that the strength of the pet-owner bond, and personal factors such as one’s level of self-awareness, will be critical modulating factors for the relationship between pet-ownership and empathy. Specifically, we predict that individuals with a stronger bond with their animal, and higher levels of self-awareness, will have higher empathy scores than those who have weaker bonds with their animals, or lower levels of self-awareness. Department: Psychology Faculty Mentor: Dr. Eric Legge
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".