Bibliographic record
Abstract
As a main direct investment instrument around the world, Private Equity (PE) is an effective method of deepening financial reform and innovation. But it is still in its initial stage in China, This text analyzes and identifies PE's risk from such dimensions as risk caused by external environment, risk from investors and invested enterprises. Analytic hierarchy process is used to qualitatively and quantitatively assess the risks, in hopes of providing the basis for risk management and control of PE． Key words: Private Equity; Risk; Analysis; Assessment Resume: L’investissement du capital prive est un outil principal et direct qu’on utilise dans le monde entier. C’est egalement un moyen efficace pour approfondir les reformes financieres et les innovations. Mais dans notre pays, il vient de commencer. Dans cet article, l’auteur fait des analyses et des identifications sur les risques poses par l'environnement exterieur, les risques des investisseurs, ainsi que les risques venants des entreprises investies. En utilisant les AHP, l’auteur evalue les risques quantitatifs et qualitatifs de l’investissement du captial prive, eu vue de fournir des appuis pour le management et le controle des risques d’investissement du captial prive. Mots-cles : investissement du captial prive; risques; analyses; evaluations 摘要：私募股權投資作為國際主流的直接投資工具，是深化金融改革與創新的有效路徑，但在我國還處於剛剛起 步的階段。本文從外部環境引起的風險、來自投資者的風險、以及來自被投資企業的風險等方面對私募股權投資 的風險進行了分析與識別，並用層次分析法對私募股權投資風險進行了定性與定量相結合的評估，以期對私募股 權投資風險管理與控制提供依據。 關鍵詞：私募股權投資； 風險； 分析； 評估
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".