Effects of the Adoption of Technology Combinations Beyond Standardized Systems on the Income of Chinese Tobacco Farmers
Bibliographic record
Abstract
Using the microdata for tobacco farmer households in Chongqing, China, this article analyses the determinants of adopting additional multiple agricultural technologies and their impact on income based on implementing a standardized technology system by a tobacco company. In this paper, selection bias from the observed and unobserved heterogeneity was corrected using a multinomial endogenous treatment effects model, and the endogenous properties were eliminated. The empirical results show that the adoption of a variety of additional agricultural technologies was determined by famer’s education level, years of tobacco planting, household size, number of technical training sessions, distance from farmer’s family to the nearest tobacco technology extension station, distance from farmer’s family to the nearest township, proportion of land suitable for machine farming, proportion of leased land. Different from empirical judgment, integrated pest management and balanced fertilization are the most effective additional technology combination strategies for increasing farmers’ income instead of combining all additional comprehensive technologies. The research results suggest that Chongqing Tobacco Company should further strengthen the training of tobacco farmers and guide tobacco farmers to take appropriate pesticide and fertilizer input beyond the standardized technical system, especially for those tobacco farmers far away from the tobacco technology extension station.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".