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Record W4380482006 · doi:10.6000/1929-4409.2020.09.247

Transformation of Resource Allocation Processes Based on Digital Technologies

2022· article· en· W4380482006 on OpenAlexvenueno aff
Suhodoeva Lyudmila Fedorovna, Nemova Olga Alekseevna, Khizhnaya Anna Vladimirovna, Svetlana Mihailovna Maltseva, Svadbina Tatiana Vladimirovna

Bibliographic record

VenueInternational Journal of Criminology and Sociology · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicEconomic and Technological Developments in Russia
Canadian institutionsnot available
Fundersnot available
KeywordsDigital transformationProcess (computing)Mechanism (biology)Resource allocationResource (disambiguation)Computer scienceKnowledge managementTransformation (genetics)Process managementBusinessRisk analysis (engineering)World Wide Web

Abstract

fetched live from OpenAlex

The article seeks to reveal the features of the transformation of the processes of resource provision of enterprises. In fact, a mechanism for providing resources based on digital technologies for the implementation of enterprise development programs has been developed. New approaches to the interaction of enterprises for creating digital models of development process management are formulated. A system of interaction between enterprises in the digital economy is proposed, which makes it possible to make decisions when allocating resources effectively. Among the main problems identified is the low efficiency of the methodological mechanism for resource allocation. Based on the outcomes, it can be concluded that the creation of digital technology is becoming one of the main advantages of the mutual linking of sources of the resource base at all levels of management.

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)0.007
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 score0.005
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0020.005
Scholarly communication0.0050.005
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.046
GPT teacher head0.308
Teacher spread0.262 · 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

Citations2
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