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Record W4392423989 · doi:10.6000/1929-4409.2020.09.341

Strategic Cooperation of Ukrainian Industrial Enterprises to Create Competitive Advantages in the World Market

2021· article· en· W4392423989 on OpenAlexvenueno aff
Fedir Perepadya, Наталья Сергеевна Захаренко, Vladimir G. Bodrov, Irina P. Zrybneva, Igor Paryzkyi

Bibliographic record

VenueInternational Journal of Criminology and Sociology · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Issues in Ukraine
Canadian institutionsnot available
Fundersnot available
KeywordsUkrainianBusinessIndustrial organizationCommerce

Abstract

fetched live from OpenAlex

Competitive advantages in the market can be accumulated both with the use of the mechanism of cooperation, and as a result of coordination in the interregional sphere. The relevance of the study is determined primarily by the fact that cooperation between organisations allows to increase competitiveness in the foreign market. However, this gives rise to a contradiction that determines that cooperation between companies is possible only if the conglomerate or certain entities own controlling shares. With that, companies should not only constitute holding structures, but primarily be co-operators in the production cycle. The novelty of the study is determined by the fact that strategic cooperation is proposed to be considered not only as a set of practical actions on the part of the state or regulatory structures, but also of consulting bodies. It is proposed to use the mechanisms of strategic cooperation based on mutual conditionality of interests and security of budgetary mechanisms that allow for practical activities. The authors also admit the possibility of the use of public-private partnership mechanisms. The practical significance of the study is determined by the fact that each of the participants in the organisation of strategic management of enterprises can use not only strategic, but financial and systemic interaction mechanisms to form.

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)0.000
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.396
Threshold uncertainty score0.674

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.108
GPT teacher head0.319
Teacher spread0.212 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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