The Comparison of Financing Efficiency of Small and Medium Enterprises in Economically Underdeveloped Regions in China: A Perspective Study
Post-publication record
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Bibliographic record
Abstract
Small- and medium-sized enterprises (SMEs) are important foundations to implement mass entrepreneurship and innovation and play an irreplaceable role in increasing employment, promoting economic growth, as well as scientific and technological innovations, and providing particularly social harmony and stability and imminently are strategic entities to the national economy and social development in underdeveloped regions. However, the low-efficiency financing of SMEs has gradually become a major factor that restricts the high-quality development of SMEs in the current conditions. In this paper, interest expenditure, gearing ratio, and the net debt ratio as input indicators and current asset turnover ratio, cost margin, and main business income as output indicators are used to conduct the DEA-BCC model. By utilizing the GEM-listed private enterprises between 2017 and 2020 in China, the nationwide financing efficiency of SMEs is firstly measured, and then the financing efficiencies of SMEs in economically developed regions and lagging regions are calculated separately. The comparison reveals that the financing efficiency of SMEs in economically underdeveloped regions is not only lower than the national average figure but also much lower than the financing efficiency level in economically developed regions, which is the result of the combined effect of internal and external factors that enterprises face. Further, this paper finds that unexpected public events, core technical personnel, and enterprise size have an impact on the financing efficiency of SMEs when running group testing. This paper puts forward rationalized suggestions to the institutions to improve the financing efficiency of SMEs in underdeveloped regions concerning the conducted research, which are called government, financial institutions, and enterprises.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".