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Record W2603326093 · doi:10.5539/ibr.v10n4p148

Corporate Culture in Russia: History, Progress, Problems and Prospects

2017· article· en· W2603326093 on OpenAlexvenueno aff
Sergei I. Chernykh, Vladimir I. Parshikov

Bibliographic record

VenueInternational Business Research · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicCultural, Linguistic, Economic Studies
Canadian institutionsnot available
Fundersnot available
KeywordsOrganizational cultureExplicationCorporate communicationState (computer science)Theme (computing)Political scienceSociologyBusinessStakeholderPublic relationsEpistemologyComputer science

Abstract

fetched live from OpenAlex

Research on corporate culture is a trend that in essence reflects the problems and risks that exist in Russian society as well as corporations. Authors have three goals: first goal is to make a short analysis after investigations, that are dedicated to the condition of Russian culture; the second goal is an analysis of risks and problems of facing the Russian state and corporations caused by the world community’s transition to a new technological environment; and the third goal is the explication of development prospects of Russian corporate culture. There are many possible differentiated approaches and methods possible for a study of a cultural phenomena. The practical aspects of the corporate culture formation are considered on the example of the finance-bank structure “Sberbank”. The most productive approach is interdisciplinary research method. The research result is that the Russian scientific community has two main approaches (related to the corporate culture theme): rational and value approach and the term “corporate culture” used in the scientific literature as adequate for definitions “organizational” and “business” culture. This is identical to western research tradition, in general. The “industrial” development level of Russian corporate culture does not correspond to the meanings and values of the sixth technological paradigm, which is the main problem and the original “risk field” for optimization of corporate culture in Russia. The authors consider the organizational forms in which corporate culture develops and the wide historic experience of professional education had been accumulated earlier and corresponded to the pragmatism as the megatrend of education development that is the positive factor.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.003
Scholarly communication0.0030.002
Open science0.0000.001
Research integrity0.0010.001
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 head0.226
GPT teacher head0.417
Teacher spread0.191 · 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 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
Published2017
Admission routes1
Has abstractyes

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