Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it