Impact of exposure on the willingness to work with dog breeds
Bibliographic record
Abstract
In 1961, Dr. Boris Levinson’s therapeutic animal work was presented to the American Psychological Association (APA), later serving as the catalysis for what is now known as animal assisted interventions (AAI) (Altschiller, 2011). However, animals were long before assisting humans physically and emotionally. Since 1961, many animals have been introduced into therapeutic settings, hospitals, schools, nursing homes, rehabilitation facilities, prisons, and more (Granger & Kogan, 2006), with dogs being the most commonly integrated animal. Addonisio (2020) found that “bad” (e.g., Pit Bull Terriers, Rottweilers, Mastiffs) and “neutral” (e.g., German Shepherd, Dalmatian) reputation dog breeds were rated significantly lower on therapeutic qualities (e.g., nonjudgmental, approachable, engaging) than “good” reputation dogs (e.g., Golden Retrievers, Labrador Retrievers). Based on the parasocial contact hypothesis (Schiappa et al., 2005), it was hypothesized that an individual’s perception of a specific dog breed would become more positive with parasocial contact (i.e., indirect contact via media). To date, no study has examined the impact of a breed specific exposure on the perception of bad reputation dog breeds. A two-way multivariate analysis of variance (MANOVA) was performed to examine the interaction effect of the dog breeds and exposure (IVs) on perceived therapeutic qualities and likelihood of working with a dog (DVs). An exploratory Pearson correlation coefficient matrix was conducted to evaluate the relationship between therapeutic qualities ratings and the five M5-120 personality domains. No significant interaction effect between exposure groups or breed of the dog on the combined dependent variables was found. There was a significant positive correlation between two personality domains (Openness to experiences and Agreeableness) and therapeutic qualities ratings. Further research is needed to explore potential modifications that may combat the negative perceptions of dog breeds that are often utilized in AAI.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".