Variable Returns to Fertiliser Use and the Geography of Poverty: Experimental and Simulation Evidence from Malawi
Bibliographic record
Abstract
J Afr Econ (2017) 26 (3): 342–371. DOI: https://doi.org/10.1093/jae/ejx002 In the original article, the Acknowledgements section was inadvertently deleted. We have added this in to the online version. It should have read as follows: We thank Todd Benson for his technical support and for making the experimental trial data available and Jacob Ricker-Gilbert, Megan Sheahan, and Mariam Mapila for providing maize and fertilizer price data. We thank Zhe Guo for GIS assistance. We thank audiences at Columbia and Cornell Universities and Marc Bellemare, Julia Berazneva, Brian Dillon, Andrew Dorward, Thom Jayne, Hope Michelson, Karl Pauw, Jacob Ricker-Gilbert, Megan Sheahan, and Scott Swinton for helpful feedback and comments on an earlier draft, and the National Natural Science Foundation of China (Award No. 71228301), Irish Aid (Malawi), and the CGIAR Research Program on Policies, Institutions, and Markets for financial support. Any errors remain solely our own responsibility.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".