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Record W1488725679 · doi:10.3968/6569

Chinese Enterprises’ Marketing Strategy Innovation Under the New Economic Environment

2015· article· en· W1488725679 on OpenAlexvenueno aff
Zheng Zhang, Wang Yu

Bibliographic record

VenueHigher education of social science · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Development and Digital Transformation
Canadian institutionsnot available
Fundersnot available
KeywordsProsperityChinaCopyingBusinessMarket economyImitationChinese economyEconomic systemFunction (biology)MarketingIndustrial organizationEconomicsEconomic growthPolitical science

Abstract

fetched live from OpenAlex

As China’s 40th reform and opening-up anniversary approaches, earthshaking changes have taken place in China’s corporate marketing. In the past 30-odd years, Chinese enterprises have taken an imitation path most of the time. However, after entering the 21st century, is the path still applicable to Chinese market? With accelerated global economic integration, does the path still meet the demands of Chinese market? Should it keep copying or open up a way of innovation? Domestic and foreign experts and scholars as well as the corporate circle provide their own opinions. China has its own conditions and market status. However, on the contrary, numerous Chinese enterprises can’t combine the path with their actual conditions, which is fatal to an enterprise. Temporary prosperity doesn’t necessarily make Chinese enterprises “evergreen trees”. Advanced economy with rich cultures is one of market economic forms. Such economy is innovative and its function is to promote constant growth and sustainable development of market economy. Hence, under the new situation of knowledge-based economy, enterprises’ ability is determined by their innovation ability.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.039
GPT teacher head0.271
Teacher spread0.232 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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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