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Record W4233980930 · doi:10.5430/rwe.v2n194

Analysis of Factors and Countermeasures on Intellectual Property Friction in China's Foreign Trade

2011· article· en· W4233980930 on OpenAlexvenueno aff
Zhijun Sheng, Haiyong Ma

Bibliographic record

VenueResearch in World Economy · 2011
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIntellectual Property and Patents
Canadian institutionsnot available
Fundersnot available
KeywordsChinaIntellectual propertyInternational tradeGovernment (linguistics)BusinessEconomicsPolitical scienceLaw

Abstract

fetched live from OpenAlex

In recent years, trade friction related with intellectual property rights in China's foreign trade get increasingly prominent, which is a huge challenge for China. In this article, we will firstly analyze the status of intellectual property friction of China's foreign trade, secondly analyze the China's factors of Chinese foreign trade friction relating to intellectual property rights, and finally put forward strategies on how to tackle intellectual property rights friction from the perspectives of government and enterprises respectively.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.245
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.303
GPT teacher head0.291
Teacher spread0.012 · 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.

Study designObservational
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
Published2011
Admission routes1
Has abstractyes

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