Bibliographic record
Abstract
Abstract In the literature on privately provided public goods, altruism has been motivated by what contributions can accomplish (public goods philanthropy), by the pleasure of giving (warm‐glow philanthropy), or by the desire to personally make a difference (impact philanthropy). Underlying these motives is the idea that individuals trust that their donations reach their goal. We revisit these models but allow for distrust in the institutional structures involved. An important result we derive is that trust considerations determine whether crowding out is less or more than complete, and we thus open up possibilities in terms of the extent of crowding out not currently available. We also model socially motivated philanthropy when income‐heterogeneous donors take trust and ability‐to‐pay into account. With ability‐to‐pay in social motivation, an important result we obtain is that low‐income donors may contribute more than high‐income donors (in both absolute and percentage‐of‐income terms), giving a potential theoretical foundation to the frequently observed “U‐shaped” pattern of giving.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".