MétaCan
Menu
Back to cohort
Record W2186638220

Comparing Organizational Structure and Design in Canadian & Russian Enterprises based in P.R. of China (Specialized in Hi-tech and Nano-technologies)

2011· article· en· W2186638220 on OpenAlexaboutno aff
Russian Federation

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicUniversity-Industry-Government Innovation Models
Canadian institutionsnot available
Fundersnot available
KeywordsChinaOrganizational structureHigh techDelegationBusinessDecentralizationJoint ventureJoint (building)Organizational architectureIndustrial organizationKnowledge managementManagementBusiness administrationPolitical scienceEngineeringEconomicsComputer scienceMarket economy
DOInot available

Abstract

fetched live from OpenAlex

Organizational Design of Canadian Enterprises Based in China (CEBC) and Russian Enterprises Based in China (REBC), which specialize on manufacturing nano-tech and hi-tech products, are being compared in this article. Generally, such enterprises are Russian joint ventures possessing significant scientific potential and aggressively penetrating into the areas of nano- and hi-tech. And Canadian enterprises having classical organizational structure and being leaders in the fields of nano- and other hi-tech. After analyzing the literature on classic organizational design, management problems of hi-tech enterprises in People’s Republic of China (PRC), general problems in management of foreign joint ventures in PRC, including some web recourses, the authors had put forward a few hypotheses. H1: Structural design of joint venture specializing in nanoand hi-tech production, is strongly connected with structure design of a university they are typically tied to. H2: Structural design and related to it main management problems of Canadian and Russian joint ventures have commonalities and differences in the ways connected with the American management model (decentralization, authority delegation).

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.111
Threshold uncertainty score0.896

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.024
GPT teacher head0.195
Teacher spread0.171 · 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 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

Explore more

Same topicUniversity-Industry-Government Innovation ModelsFrench-language works237,207