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Record W4380481886 · doi:10.6000/1929-4409.2020.09.303

Economic Analysis Positioning of Risks at the Stages of Company Development

2022· article· en· W4380481886 on OpenAlexvenueno aff
Albina Dzhavdatovna Khairullina, Anastasiya Alexandrovna Skutelnik

Bibliographic record

VenueInternational Journal of Criminology and Sociology · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicEconomic and Technological Developments in Russia
Canadian institutionsnot available
FundersKazan Federal University
KeywordsBusinessOperations managementRisk managementStage (stratigraphy)Product life-cycle managementLife-cycle hypothesisRisk analysis (engineering)Actuarial scienceMarketingEconomicsFinance

Abstract

fetched live from OpenAlex

The purpose of the article is economic analysis and compare the organization's risks to the stages of the life cycle according to I. K. Adizes. This paper uses risk classifications of V. S. Romanov and Microsoft. Based on the description of each stage of the organization's development, an attempt is made to systematize risks at each stage of the company's life cycle according to the model of Yitzhak Adizes. In addition, risk maps have been drawn up for the company's development stages. The authors of the article argue the need for this research by the fact that at the moment risk management in companies occurs without taking into account the life cycle, respectively, those groups of risks that are characteristic of the stage of development of the company are overlooked. This is why life-cycle risk management is necessary: companies will have a list of risks that are specific to the stage of development of the organization.

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)0.005
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: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.092
GPT teacher head0.368
Teacher spread0.277 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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Same venueInternational Journal of Criminology and SociologySame topicEconomic and Technological Developments in RussiaFrench-language works237,207