Micro insights on the pathways to agricultural transformation: Comparative evidence from Southeast Asia and Sub‐Saharan Africa
Bibliographic record
Abstract
Abstract Most studies of agricultural transformation document the impact of agricultural income growth on macroeconomic indicators of development. Much less is known about the micro‐scale changes within the farming sector that signal a transformation precipitated by agricultural income growth. This study provides a comparative analysis of the patterns of micro‐level changes that occur among small‐holder farmers in Uganda and Malawi in Sub‐Saharan Africa (SSA), and Thailand and Vietnam in Southeast Asia (SEA). Our analysis provides several important insights on agricultural transformation in these two regions. First, agricultural income in all examined countries is vulnerable to changes in precipitation and temperature, an effect that is nonlinear and asymmetric. SSA countries are more vulnerable to these weather changes. Second, exogenous increases in agricultural income in previous years improve non‐farm income and trigger a change in labor allocation within the rural sector in SEA. However, this is the opposite in SSA where the increase in agricultural income reduces non‐farm income, indicating a substitution effect between farm and non‐farm sectors. These findings reveal clear agricultural transformation driven by agricultural income in SEA but no similar evidence in SSA.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".