OPTIMAL EVALUATION INDEX SYSTEM AND BENEFIT EVALUATION MODEL FOR AGRICULTURAL INFORMATIZATION IN BEIJING
Bibliographic record
Abstract
The level of informatization is an important indicator of a country or region's level of economic development, and many domestic and foreign scholars have studied this topic.Informatization is seen as a developing social phenomenon, with both national and regional characteristics, therefore the foreign standard systems are not entirely suitable for the development of an information society in China.This paper establishes new indexes and designs an optimal evaluation index system according to the characteristics of informatization.The optimal index system is more suitable for the current development of informatization in China.Using this index system, we measure the informatization level in Beijing from 2003 to 2012.Using the Cobb-Douglass model, we construct an information benefit evaluation model to verify the positive effect of informatization on economic development in Beijing.To further study the relationship between the informatization evaluation index and the urban-rural income gap, we conduct a regression between the information evaluation index and the urban-rural income gap.It provides a quantitative scientific basis for the study of the impact of information technology on economic and social development plans, enabling improved government decision-making.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".