Bibliographic record
Abstract
Dans un contexte de crise des finances publiques, les Etats cherchent a encourager les financements volontaires alternatifs a la taxation pour financer les biens publics comme les institutions culturelles, l'education, la recherche… Ils ont donc mis en place depuis une quinzaine d'annees des incitations fiscales au don, avec l'espoir d'atteindre un niveau de financement prive similaire a celui des Etats-Unis, ou le niveau de philanthropie est beaucoup plus eleve qu'en Europe. Ainsi en France, un euro de don ouvre aujourd'hui le droit a une reduction d'impots comprise entre 0,66 et 0,75 euro. Alors que les depenses publiques associees a ces incitations augmentent, il faut s'interroger sur l'efficacite de tels dispositifs : dans quelle mesure l'Etat peut-il encourager la charite privee au moyen d'incitations financieres ?Pour repondre a cette question, il faut analyser et comparer les niveaux de dons dans les differents pays. Apres avoir rassemble les sources disponibles pour etudier le financement des biens prives autour du monde, en comparant en particulier l'evolution des dons en France, aux Etats-Unis, au Canada et au Danemark, les auteurs cherchent a comprendre les motivations des donateurs puis analysent l'efficacite des incitations fiscales au don, prenant en compte le fait que les incitations financieres sont parfois contreproductives dans le cas de la charite privee. Comment ameliorer le dispositif existant ?
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.052 | 0.006 |
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".