Bibliographic record
Abstract
Entrepreneurs are not the only ones to salivate at the call of emerging markets. Major nonprofit organizations that raise money in high‐income Organisation for Economic Co‐operation and Development countries for redistribution in developing countries are also looking for new sources of funds. Some of the countries that benefited from private philanthropy not too long ago are now in a position to help. The contribution of this paper is two‐prong. First, it introduces a robust market screening methodology to determine countries' capacities to give on the basis of macroeconomic and infrastructure indicators. Second, it identifies cross‐cultural predictors for charitable donations taken from the World Value Survey. A logistic regression calibrated on already successful fundraising operation scores countries on their propensity for private philanthropy. Research findings receive theoretical support from Max Weber's Protestant Ethic and the Spirit of Capitalism. Top countries for private philanthropy are of Protestant tradition and are at the origin of capitalism, as we know it today. Confucian countries that share a similar ethic and have opted for capitalist economies are first class candidates for private philanthropy. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.004 |
| 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 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.002 |
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".