Bibliographic record
Abstract
We are the first to examine empirically if the presence of minority individuals affects the decision to give to charities by majority individuals. We focus on two giving decisions by the majority population. The first is giving to any charitable organization; the second is giving to organizations geared to international causes. Our findings suggest that the larger the proportion of minorities in a given community, the more likely that members of the majority group living in that community give to international causes. But, for the decision to give in general, the opposite holds true: the presence of minorities exerts a negative influence on this decision, consistent with Putnam’s, and others, finding that living in a heterogeneous community has a deleterious effect on charitable giving (Alesina & La Ferrara, 2000 & 2002). / Nous sommes les premiers à étudier empiriquement si la présence d’individus appartenant à une minorité visible affecte la décision des membres de la majorité de donner à des oeuvres caritatives. Nous nous concentrons sur deux types de dons : les dons faits à tous les organismes de bienfaisance et ceux faits aux organismes défendant une cause internationale. Nos résultats suggèrent que plus la proportion d’individus appartenant à une minorité visible dans une communauté est grande, plus les gens de la majorité vivant dans cette communauté sont enclins à donner aux oeuvres caritatives dédiées aux causes internationales. Toutefois, c’est le contraire pour la décision de faire un don en général. La présence d’individus appartenant à une minorité visible affecte négativement la décision de faire un don. Ce résultat est similaire à celui de Putnam et d’autres chercheurs qui ont trouvé que vivre dans une communauté hétérogène a un effet négatif sur les dons aux oeuvres caritatives (Alesina & La Ferrara, 2000 & 2002).
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".