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Record W1997988650 · doi:10.5539/ass.v11n7p371

Managerial Decision-Making Oriented Towards Achieving Results

2015· article· en· W1997988650 on OpenAlexvenueno aff
Irina Gennadyevna Sevastyanova, Vasiliy Stegniy

Bibliographic record

VenueAsian Social Science · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicEconomic and Technological Developments in Russia
Canadian institutionsnot available
Fundersnot available
KeywordsDelegateDecentralizationBusiness decision mappingKey (lock)Making-ofManagement scienceDecision engineeringKnowledge managementBusinessProcess managementComputer scienceDecision support systemPolitical scienceEconomicsArtificial intelligence

Abstract

fetched live from OpenAlex

This article identifies the major trends in the development of modern organizations. The article examinesorganizational issues in effective managerial decision-making; describes model decision-making representations,which integrate the concept, techniques, and practical recommendations; brings to light the social factorsinfluencing managerial decision-making; identifies the key issues in innovation decision-making; provides arationale for the need to delegate powers for fast decision-making in a dynamic environment; concretizes thepotentialities of organizations that employ the decentralization experience in managerial decision-making; bringsto light the need for a match between powers and responsibility. The article concludes that the success oforganizations depends on making decisions as warranted by specific circumstances.

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)0.053
metaresearch head score (Gemma)0.053
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.053
Threshold uncertainty score0.279

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0530.053
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0040.011
Scholarly communication0.0130.007
Open science0.0030.006
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0080.003

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 head0.035
GPT teacher head0.340
Teacher spread0.305 · 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 designNot applicable
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
Published2015
Admission routes1
Has abstractyes

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