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Record W6927540727 · doi:10.34983/dtipb.2020.33.51.001

ОСНОВА ФОРМИРОВАНИЯ ФИНАНСОВОЙ СТРАТЕГИИ ПРЕДПРИЯТИЯ

2020· article· ru· W6927540727 on OpenAlexaff

Bibliographic record

VenueRussian Agency for Digital Standardization · 2020
Typearticle
Languageru
FieldEngineering
TopicMilitary Technology and Strategies
Canadian institutionsSKiN Health
Fundersnot available
KeywordsProcess (computing)Identification (biology)Product (mathematics)

Abstract

fetched live from OpenAlex

Аннотация. В статье раскрыты сущность финансовой стратегии, показана ее направленность на обеспечение перспективного развития финансовой деятельности предприятия в условиях неопределенности внутренней и внешней экономической среды. Отмечено, что финансовая стратегия является одной из функциональных стратегий предприятия. Определены основные объекты и методы финансового анализа и прогнозирования, ключевые группы финансовых показателей. Исследовано значение эффективного применения на предприятии финансового планирования и прогнозирования, их важность в формировании финансовой стратегии предприятия. Сделаны выводы о том, что финансовый анализ и прогнозирование являются основой обеспечения финансовыми ресурсами, достижения финансовой стабильности и развития предприятия

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)0.003
metaresearch head score (Gemma)0.008
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 score0.032
Threshold uncertainty score0.108

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0030.007
Scholarly communication0.0120.007
Open science0.0010.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0320.012

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 head0.014
GPT teacher head0.222
Teacher spread0.207 · 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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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