Does digital literacy affect farmers’ adoption of agricultural social services? An empirical study based on China Land Economic Survey data
Bibliographic record
Abstract
Agricultural social services (ASS) are crucial in alleviating resource constraints and advancing agricultural modernization. Using the data derived from the China Land Economic Survey (CLES2022), this study empirically investigates how digital literacy influences farmers' adoption of ASS, employing both Probit and Propensity score matching (PSM) models. Additionally, it explores the mediating roles of long-term production vision and part-time employment degrees in this relationship. The findings are: (1) digital literacy exhibited a statistically significant positive effect on farmers' adoption of ASS at the 1% significant level. Moreover, this impact varied among participants in technology training, different education levels, and varying levels of risk preference; (2) long-term production vision and part-time employment degrees act as mediators, enhancing the positive impact of digital literacy on farmers' adoption of ASS. Based on these findings, recommendations have been developed to improve farmers' digital literacy, promote the adoption of ASS, and enhance farmers' long-term production vision as well as degree of part-time.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".