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Develop Venture Capital Industry, Perfect Science and Technology Investment & Financing System in Shenyang

2009· article· en· W1943359601 on OpenAlexvenueno aff
Yaxin Zhang, Jingting Ma

Bibliographic record

VenueCanadian social science · 2009
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPrivate Equity and Venture Capital
Canadian institutionsnot available
Fundersnot available
KeywordsVenture capitalSocial venture capitalInvestment (military)BusinessFinanceCapital (architecture)Political science

Abstract

fetched live from OpenAlex

Thriving the development of Venture Capital industry contains great significance in perfecting science & technology investment and financing system and accelerating the development of high-tech industry of Shenyang. Currently, restrained by multiple factors such as scale, quit channels and exterior environment, etc, of Venture Capital , it plays only a limited role in solving the financing difficulty of science & technology enterprises of Shenyang. It shall emphasize on the following points if it is intended to develop and expand the Venture Capital industry in Shenyang, that is, strengthen the role of government Venture Capital organs as models and power house; foster and develop Venture Capital organs in an active way; perfect multi-layered capital market and broaden the quit channel for Venture Capital ; perfect intermediary service system and promote investment efficiency in Venture Capital ; foster the great cultural atmosphere for starting the business, etc. Key words: Science & Technology Financing; Venture Capital ; Capital Market Resume: La prosperite du developpement de l'industrie de capital a risque a une grande importance pour le perfectionnement de l’investissement dans science & technologie et le systeme de financement et pour l’acceleration du developpement de l'industrie high-tech de Shenyang. Actuellement, contraint par de multiples facteurs du capital a risque, tels que l'echelle, les mecanisme de retrait et l'environnement exterieur, etc, il ne joue qu'un role limite dans la resolution des difficultes de financement des entreprises de science & technologie de Shenyang. Il doit mettre l'accent sur les points suivants si on veut developper et promouvoir l'industrie de capital a risque a Shenyang, qui sont, renforcer le role du gouvernement; favoriser et developper des entreprises de capital a risque de maniere active; perfectionner un marche des capitaux multi-couches et les mecanismes de retrait pour le capital a risque; ameliorer le systeme de services intermediaires et promouvoir l'efficacite des investissements en capital a risque; favoriser l'atmosphere culturelle pour les demarches de creation des entreprises, etc. Mots-Cles: Financement pour la Science et la technologie; capital a risque; marche du capital

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.906
Threshold uncertainty score0.854

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.006
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.213
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2009
Admission routes1
Has abstractyes

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