The Selection of Service Model and Development Path of Informationization in Rural Areas: Based on the data of Chongqing City in China
Bibliographic record
Abstract
The informationization of the rural areas is a complex systemic project because of its long period of cost-recovery, slow effects and high risks in investment. On one hand, it has its advantageous realistic base, increasingly improved environment, better infrastructures and information-based service; on the other hand, there are many bottlenecks of the inequality in public service. Therefore, the informationization of the rural areas in China should be carried out step by step in different regions. Evidently, it is meaningful to explore, test and promote a service mode suitable for Chinese rural areas by concluding the practices of informationization in the rural areas of Chongqing and learning from the advanced concepts and successful experiences of the developed areas. By improving the information-based service system and balancing the demand and supply of the information in Chinese rural areas, the key problems like, the production, marketing and delivery of crops products can be solved and the goal of promoting the equalization of the government’s public service can be achieved ultimately.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".