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Record W2262059373 · doi:10.5539/ijef.v8n2p39

Technological Embedding, Industrial Integration, Industrial Upgrading----Discuss about the Role of Information Technology in Industrial Upgrading and Transformation

2016· article· en· W2262059373 on OpenAlexvenueno aff
Lin Xue-jun, Hao Luo, He Nie

Bibliographic record

VenueInternational Journal of Economics and Finance · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Technological Innovation
Canadian institutionsnot available
FundersJinan University
KeywordsProductivityIndustrial organizationService (business)Manufacturing engineeringIndustry 4.0ManufacturingBusinessInformation technologyProduction (economics)Tertiary sector of the economyInformation industryComputer scienceEngineering managementEngineeringMarketingEconomicsEconomyEconomic growth

Abstract

fetched live from OpenAlex

<p>At present, transformation and upgrading traditional industries are key to our country’s economic development. Discussion on how to transform traditional industries utilizing the information technology is hot in academic world. Transformation of traditional industries using information technology can fall into three categories: total Integration, embedded Integration, and general Integration. The result is to form a new industry, transform traditional industries, or increase the original industry productivity respectively. German industry version 4.0 is a case in point of industry Integration through intelligent factories, smart production, intelligent network, intelligent service etc. to build a smart manufacturing industry. China should vigorously utilize “Internet +” to upgrade China’s manufacturing industry, through which intelligent factories improve enterprise’s productivity, intelligent production increases the productivity of the industry, intelligent network improves the productivity of the whole society, intelligent service improves the economic vitality of the whole society, hence build the Chinese industry version 4.0.</p>

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)0.001
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.211
Threshold uncertainty score0.449

Codex and Gemma teacher scores by category

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

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 head0.038
GPT teacher head0.232
Teacher spread0.194 · 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

Citations3
Published2016
Admission routes1
Has abstractyes

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