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Record W2901739579 · doi:10.6000/1929-7092.2018.07.62

Value and Price of Russian Business: The Entity, the Relationship and Impact of Financial Indicators

2018· article· en· W2901739579 on OpenAlexvenueno aff
Kosorukova Irina, Plyasova Svetlana, Prokimnov Nikolay, Rodin Alexander

Bibliographic record

VenueJournal of Reviews on Global Economics · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicEconomic and Technological Developments in Russia
Canadian institutionsnot available
Fundersnot available
KeywordsValue (mathematics)BusinessEconomicsMathematicsStatistics

Abstract

fetched live from OpenAlex

Topicality: In the article the authors analyze the relationship between the book and market value and the price (level of capitalization) of the business, and also analyze the nature of the relationship of these indicators with the traditional financial ratios.The purpose of the article is to determine the state of these relations in the modern economy of Russia.Practical Usefulness: Determination of materiality influence of financial coefficients which used in traditional financial analysis on value and price of business that gives the chance of forecasting of development and decision-making within management of business` value, and also more effective investment of means in securities.Novelty: In modern Russian economic conditions, only part of the traditional financial ratios has an impact on the prices and business value.The analysis presented in the article indicates the feasibility of forming such a system of indicators, which is more informative in terms of their use for the implementation of value-based analysis and approach for decisionmaking in 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)0.006
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 score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0000.001
Scholarly communication0.0030.002
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.034
GPT teacher head0.330
Teacher spread0.296 · 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
Published2018
Admission routes1
Has abstractyes

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Same venueJournal of Reviews on Global EconomicsSame topicEconomic and Technological Developments in RussiaFrench-language works237,207