NBER WORKING PAPER SERIES DOES DECENTRALIZATION FACILITATE ACCESS TO POVERTY-RELATED SERVICES? EVIDENCE FROM BENIN
Bibliographic record
Abstract
2009. We are grateful to the Municipal Development Partnership (MDP) in Cotonou, with special thanks to Hervé Agossou, for their warm welcome, and valuable assistance in collecting data, as well as for their fruitful comments and discussions. We also thank the Benin National Institute of Statistics and Economic Analysis, especially Cosmé Vodounou and Damien Mededji, for allowing us access to EMICoV surveys. We thank Elias Potek (University of Montreal, Geography Dept.) for his outstanding work in creating geographical maps in record time. We thank Simon Johnson (MIT) who acted not only as a scientific mentor throughout this research e¤ort, but also as a valuable advisor. We warmly thank Michael Hiscox (Harvard University) for the valuable comments that helped make the paper's final version stronger and Antoinette Sayeh (IMF) for handling the paper's policy concerns when it
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 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".