Production Diversity and Socioeconomic Characteristics of Household Farms
Why this work is in the frame
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Bibliographic record
Abstract
The level of production diversity chosen by small household farms may not be optimal from a social perspective, due to the existence of market failures such as environmental externalities or barriers to credit. Public policies designed to stimulate more diversified crops are supposed to correct that inefficiency. Understanding the socioeconomic characteristics associated with agricultural diversification is important for a successful implementation of those policies. In this paper we investigate which are those characteristics that are mostly related with crop diversification. Unlike previous studies, which use small samples, circumvented to small geographical areas, we address these issues with a large and comprehensive dataset, with observations spread through a large geographical dimension, making it possible to analyze the role played by regions. We take a group of 4.7 million Brazilian farm households, of which a random sample is extracted and used in the estimation procedures. We then estimate a Tobit regression model using key agricultural variables and the well-known Simpson Diversification Index to measure crop diversification. The main findings are that the region where the farm is located, the on and off farm incomes, the farm’s size, the access to technical assistance, the farmer’s age and education all play important roles in explaining production diversity. Public policies will more likely achieve crop diversification if they take into account those characteristics.
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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 | 0.001 |
| 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