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Record W4407773884 · doi:10.34755/irok.2021.71.41.015

Зарубежный опыт развития системы социальной сферы

2021· article· ru· W4407773884 on OpenAlexaboutno aff
С.М. Касымова, Б.И. Омошева, Ф.С. Парпиева

Bibliographic record

VenueAktualʹnye voprosy sovremennoj èkonomiki. · 2021
Typearticle
Languageru
FieldSocial Sciences
TopicCentral Asia Education and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

В статье рассмотрены опыт зарубежных стран по развитию системы социальной сферы, уточнены их теоретические и практические положения. Предметом исследования выступают социальная сфера зарубежных стран. По результатам исследования на основе исследования опыта зарубежных стран, в частности США, Канады по развитию системы социальной сферы предложены пути применения их опыта на практику Кыргызской Республики. The article considers the experience of foreign countries in the development of social systems, clarifies their theoretical and practical positions. The subject of research is the social sphere of foreign countries. According to the results of the study based on the experience of foreign countries, in particular the United States, Canada on the development of the social sphere are proposed ways to apply their experience to the practice of the Kyrgyz Republic

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 score0.023
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0030.008
Scholarly communication0.0100.005
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0230.006

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 head0.027
GPT teacher head0.304
Teacher spread0.277 · 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
Published2021
Admission routes1
Has abstractyes

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