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Record W4327604702 · doi:10.18280/ijsdp.180226

Stages of Digital Transformation of Educational Institutions in the System of Sustainable Development of the Region

2023· article· en· W4327604702 on OpenAlexvenueno aff
Svitlana Kryshtanovych, Galyna Liakhovych, О. М. Дуброва, Henrikh Kazarian, Ganna Zhekalo

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2023
Typearticle
Languageen
FieldComputer Science
TopicEducational Innovations and Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsSustainable developmentTransformation (genetics)Digital transformationEnvironmental planningBusinessPolitical scienceComputer scienceEnvironmental scienceWorld Wide WebChemistry

Abstract

fetched live from OpenAlex

The purpose of the article is to model the main stages of sustainable development through the digital transformation of educational institutions in the region.World trends in the development of society, education, science and economy are aimed at the transition to digital transformation.Successful digitalization is becoming a key condition for the successful functioning of both the region and the entire country.Accordingly, today the successful sustainable development of the region is impossible without effective digitalization of higher education.Based on the results of the study, paired models of sustainable development of the region were formed with the help of digital transformations, taking into account the characteristics of the educational environment.The study has a number of limitations related to the inability to cover more than one region.Subsequent research should be related to ensuring the sustainable development of the region through the digitalization of other areas of activity.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.011
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0020.005
Scholarly communication0.0060.006
Open science0.0010.004
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 head0.042
GPT teacher head0.296
Teacher spread0.254 · 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

Citations8
Published2023
Admission routes1
Has abstractyes

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