Financing Mode of American Small and Medium-sized Enterprises and the Enlightenment to Our Country
Bibliographic record
Abstract
Because of the characteristic of small and medium-sized enterprises such as small scale of assets, low transparency message and heavy uncertainty of management, the financing mode of small and medium-sized enterprises of various countries is the same basically. This paper reflect the characteristic of the financing mode of small and medium-sized enterprises of our country indirectly through studying the financing mode and characteristics of American small and medium-sized enterprises, expecting to enlighten alleviating the difficulty of financing of small and medium-sized enterprises of our country . Key words: Financing mode, Characteristic, Enlightenment Resume : La modele de fusion des petites et moyennes entreprises dans tous les pays existe des points communs , en raison de ses quelques caracteristiques suivantes : les capitaux de petite envergure , la transparence insuffisante des informations et l’instabilite de gestion . A travers la recherche sur la modele de fusion des petites et moyennes entreprises aux etats-unis , ce texte reflete de facon indirecte les traits de modele de fusion des petites et moyennes entreprises en Chine dans l’espoir de donner l’inspiration a l’attenuation des difficultes de la fusion des petites et moyennes entreprises de notre pays . Mots-cles: modele de diffusion, traits , inspiration 摘要：由於中小企業具有資產規模小，資訊透明度低，經營不確定性大等特徵，各國中小企業融資模式存在一定的共同之處。本文通過研究美國中小企業融資模式及其特徵，間接反映了我國中小企業融資模式的特點，期望對緩解我國中小企業融資困難有所啟示。 關鍵詞：融資模式；特徵；啟示
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".