Bibliographic record
Abstract
In view of the development problems of banks at village and town levels,through introducing the concept of private relationship lending,the functions of soft information,the channels of banks at village and town level for collecting soft information,and the private relationship lending of banks at village and town level under Chinese rural human environment of highlighting relationship while despising rationality are proved.According to the recognition standard of core competitive edge,it can be concluded that the core competitive edge of banks at village and town level is private relationship lending.In the first place,these kinds of small and medium-sized quarter banks have competitive advantages in launching private relationship lending in the second place,the lending businesses of banks at village and town level based on soft information attracts small and medium clients;in the third place,the private relationship lending has realized the scale economy.Furthermore,the reasons why banks at village and town level can not display the core competitive edge have been analyzed:firstly,banks at village and town level have not found that private relationship lending is their core competitive edge;secondly,the internal motivation on establishing private relationship lending of banks at village and town level is insufficient;thirdly,banks at village and town level have not prepared well in developing private relationship lending;The relevant policies and countermeasures are put forward,which including transforming idea and vigorously developing private relationship lending;intensifying training and improving the quality of personnel involved;strengthening supervision and avoiding the violation behaviors of personnel involved;mirroring experiences and perfecting the private relationship lending mechanism of banks at village and town level.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".