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Record W2102428338 · doi:10.4236/ti.2015.62010

The Research on Current Situation and Countermeasure of University Science Park in Gulou District of Nanjing, China

2015· article· en· W2102428338 on OpenAlexvenueno aff
Songqiang Wu, Xiao Xiao, Wang Lu, Xingyi Shen, Xianting Tao

Bibliographic record

VenueTechnology and Investment · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicUniversity-Industry-Government Innovation Models
Canadian institutionsnot available
FundersNational Social Science Fund of ChinaGovernment of Jiangsu Province
KeywordsScience parkChinaBottleneckCountermeasureService (business)Sustainable developmentState (computer science)Regional scienceFunction (biology)BusinessPolitical scienceEconomic growthEngineeringEconomicsMarketingGeographyOperations managementComputer science

Abstract

fetched live from OpenAlex

So far, the development of State University Science Park has become important force to promote regional economy development. This paper theoretically makes judgment over the effects of regional economy development of State University Science Park. Thus, taking State University Science Park of Gulou District in Nanjing as an example, the paper respectively elaborated the influence of State University Science Park on regional economic brands, services and information from the aspects of brand building, technology trade, and scientific and technical activities undertaking. In order to further improve and popularize the practices of State University Science Park of Gulou District in Nanjing, the paper proposed the corresponding policy inspiration: constantly stuck to the law of economic development and innovated the service function and approaches; made use of science and technology entering the park to organize innovation union to promote the capacity of regional scientific and technical innovation and broke the bottleneck of State University Science Park development to provide the regional economy with sustainable service.

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 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 score0.702
Threshold uncertainty score0.342

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.071
GPT teacher head0.285
Teacher spread0.214 · 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
Published2015
Admission routes1
Has abstractyes

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