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Record W2598255251

DYSFUNCTION AS AN OBSTACLE IN ACHIEVING SUSTAINABLE SOCIAL ECOLOGICAL ECONOMIC DEVELOPMENT OF THE REGION (EVIDENCE FROM THE VOLGA FEDERAL DISTRICT)

2016· article· en· W2598255251 on OpenAlexvenueno aff
Evgeniya Vladimirovna Kabitova, Svetlana Anatolievna Ashirova, Svetlana Mazgutovna Nuriyahmetova, Olga Aleksandrovna Fathutdinova, A. N. Ashirov

Bibliographic record

VenueThe Journal of Internet Banking and Commerce · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicDigitalization and Economic Development in Agriculture
Canadian institutionsnot available
Fundersnot available
KeywordsSustainable developmentDysfunctional familyObstacleConformityReproductionEnvironmental resource managementBusinessEnvironmental planningPolitical scienceEcologyGeographyEconomicsLawPsychologyBiology
DOInot available

Abstract

fetched live from OpenAlex

The creation of a society with a vector of sustainable development predetermines the search for optimal proportions of reproduction of social ecological economic parameters of the territories. The article presents the development results of sustainable social ecological economic advancement model of the regional system and verifies the appropriateness of the social ecological economic systems’ parameters of the regions of the Volga Federal District (VFD) to the proposed sustainable development model. We introduce the notion of dysfunction (dysfunctional development) considered as an obstacle and antipode of sustainable development of the territory, and define the boundaries of normally occurring processes and the dysfunctionality for the VFD regions. We performed calculation and evaluation of the territories in terms of their conformity with sustainable development or dysfunction. The proposed method can be positioned as a way of identifying and adjusting dysfunctional territories and can be applied for further definition of effective response tools.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.003
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.217
Teacher spread0.194 · 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 designObservational
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

Citations0
Published2016
Admission routes1
Has abstractyes

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Same venueThe Journal of Internet Banking and CommerceSame topicDigitalization and Economic Development in AgricultureFrench-language works237,207