Supply Chain Financial Risk Evaluation of Small- and Medium-Sized Enterprises under Smart City
Bibliographic record
Abstract
Prevention and control of risks are an eternal theme of financial institutions. Although, to some extent, the emergence of supply chain finance can enhance the financing capacity of small- and medium-sized enterprises (SMEs) and reduce financial risks of financial institutions, with the development of smart city and smart finance, the financial risks of SMEs are more complex, infectious, dormant, and difficult to accurately identify and measure. Facing this status, financial institutions have been required to understand and evaluate the financial risks of SMEs from a new perspective. Therefore, this paper, based on the study of financial risks assessment of SMEs under the smart city and smart finance, innovatively constructs a new index evaluation system for supply chain finance, based on improved hesitant fuzzy linguistic PROMETHEE method, and the effectiveness and advantages of the model have been verified through an example. To a certain degree, the SMEs financing the evaluation model and improved PROMETHEE method can not only help financial institutions reduce the risks in the specific financial transactions but also reduce the survival threat of financial institutions. Moreover, it is of positive significance to the stable operation of the financial system.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".