Bibliographic record
Abstract
Growing public concern for the welfare of animals is reflected in an increase in the number of animal charities around the world. However, little is known about the individuals who donate to these organizations. In this study, we examine relations between individual differences in personal values and sociodemographic characteristics and the decision to donate to animal charities. We do this in samples from nine different countries: the USA, Canada, Australia, the Netherlands, Italy, Poland, Malaysia, Singapore, and China. We show that the personal value expressing concern for the welfare of animals is empirically distinct from other refined values and that this value is positively associated with giving to animal charities in each country. These results extend recent attempts to identify and validate the animals value as a distinct value beyond western samples. Using logistic regression analysis, we also show, in all nine country samples, that the animals value is the most consistent predictor of donating to animal charities when compared with sociodemographic characteristics examined in previous studies. The results of this study can be used by organizations in the animal protection sector to inform their donor segmentation and targeting strategies both within and across borders.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".