The fruits (and vegetables) of crime: Protection from theft and agricultural development
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Fear of crime is a concern in developing countries where rule of law is imperfectly enforced. I use a cluster-randomized field experiment in Kenya to show that reducing fear of theft allows small-scale farmers to adjust their planting and time use decisions, as well as increasing crop yields. I randomly allocated subsidized watchmen to farmers in Kenya, reducing their perceived risk of theft. Farmers offered watchmen were 14 p.p. more likely to have crops they grew for the first time or grew on more land as a result of improved security, sold more crops off-farm, and their farm output per acre was larger by 15% of the control mean. The intervention had positive security spillovers, and led to fewer angry disputes among neighbours. Despite these benefits, this intervention is not profitable for an individual farmer, suggesting a potential role for collective security interventions.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it