NBER WORKING PAPER SERIES DECENTRALISATION IN AFRICA AND THE NATURE OF LOCAL GOVERNMENTS ' COMPETITION: EVIDENCE FROM BENIN
Bibliographic record
Abstract
2009. We are grateful to the members of the Municipal Development Partnership (MDP) in Cotonou, especially Hervé Agossou, for their warm welcome, their valuable help in collecting data, their fruitful comments, and their discussions. 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 has acted not only as a scientific mentor throughout our researching endeavors, but also as a valuable advisor. We thank Odd-Helge Fjeldstad (International Centre for Tax and Development) and François Vaillancourt (University of Montreal) for all of their helpful suggestions. We are grateful to Leonard Wantchekon (Princeton University) and the participants at the IREEP (Institut de Recherche Empirique en Economie Politique) conference, the CERDI (Centre d'Etudes et de Recherche sur le Développement International) seminar, and the CIRANO (Centre Interuniversitaire de Recherche en Analyse des Organisations) workshop, where a preliminary draft of this paper was presented in November 2010. Finally, we acknowledge financial support from the NBER Program on African Successes, especially Elisa Pepe for her amazing support throughout this project. Any remaining errors are ours. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.¸˛¸˛¸˛ NBER working papers are circulated for discussion and comment purposes. They have not been peer-reviewed or been subject to the review by the NBER Board of Directors that accompanies official NBER publications.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 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".