Can Agriculture-Related Enterprises’ Green Technological Innovation Ride the “Digital Inclusive Finance” Wave?
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
Abstract This article systematically examines how digital inclusive finance drives green technological innovation in agriculture-related enterprises. The findings reveal three key insights. First, digital inclusive finance fosters both the “quantity expansion” and “quality enhancement” of green technological innovation in agriculture-related enterprises. Second, this effect operates through three mechanisms: improving financial accessibility, reducing information asymmetry, and stimulating innovation vitality. Third, the impact of digital inclusive finance is heterogeneous – its driving effect is more pronounced in non-state-owned enterprises, highly digitally transformed firms, businesses in non-heavily polluting industries, and enterprises located in eastern China. In summary, this article uncovers an important channel through which digital inclusive finance promotes green technological innovation in agriculture-related enterprises and provides empirical evidence for relevant policy formulation.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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