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Record W2104721420 · doi:10.5430/bmr.v1n4p117

Countermeasures Research on Developed Countries Using Intellectual Property to Promote the Development of Strategic Emerging Industries

2012· article· en· W2104721420 on OpenAlexvenueno aff
Jie Lin, Yuzheng Han

Bibliographic record

VenueBusiness and Management Research · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Technological Innovation
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessEmerging marketsIntellectual propertyPromotion (chess)ChinaIndustrial organizationStrategic planningStrategic managementStrategic developmentStrategic financial managementEconomic systemEconomicsMarketingPolitical scienceFinanceBusiness administration

Abstract

fetched live from OpenAlex

The strategic emerging industry is the industry that emerges in one country or region along with the implementation of technical innovation achievements and has strategic effects on economic and social development. To develop the strategic emerging industries is a significant action to promote the upgrading of industrial structure and transform the pattern of economic growth, as well as a strategic option for following the new scientific and technological revolution and dealing with the international financial crisis. For Japan, the U.S., Germany, and other developed capitalist countries, the main experiences for the development of strategic emerging industries are: highly integrate the intellectual property strategy with the development strategy of strategic emerging industries, apply the dynamic management, and smoothly connect the new ideas of the strategic emerging industries with the new market. In China, in order to develop the strategic emerging industries, we should learn from the successful experiences of developed countries, draft the promotion strategy of intellectual property at the national and industrial levels, provide protections for the development of strategic emerging industries, and effectively promote the development of strategic emerging industries.

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)0.006
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 score0.935
Threshold uncertainty score0.465

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
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 head0.582
GPT teacher head0.394
Teacher spread0.188 · 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

Citations1
Published2012
Admission routes1
Has abstractyes

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