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Record W6911179705 · doi:10.5281/zenodo.10003237

СОВРЕМЕННЫЕ МЕТОДЫ ПРЕПОДАВАНИЯ ДЛЯ РЕСТРУКТУРИЗАЦИИ УЧЕБНОЙ ПРОГРАММЫ БИЗНЕС- ОБРАЗОВАНИЯ В ТЕХНИКУМАХ ДЛЯ УСТОЙЧИВОГО РОСТА, РАЗВИТИЯ И ГЛОБАЛЬНОЙ АКТИВНОСТИ

2023· article· ru· W6911179705 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2023
Typearticle
Languageru
FieldEngineering
TopicMilitary Technology and Strategies
Canadian institutionsImpact
Fundersnot available
KeywordsProcess (computing)Identification (biology)Product (mathematics)

Abstract

fetched live from OpenAlex

В этом исследовании оценивалась степень применения современных методов преподавания для реструктуризации учебной программы бизнес-образования в целях устойчивого развития и глобальной актуальности, как ее воспринимают преподаватели бизнес-образования в университетах. В основе исследования лежал один исследовательский вопрос и две гипотезы. В этом исследовании использовался дизайн опросного исследования

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.040
Threshold uncertainty score0.134

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0030.004
Scholarly communication0.0070.004
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0400.011

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 head0.032
GPT teacher head0.231
Teacher spread0.199 · 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 designTheoretical or conceptual
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
Published2023
Admission routes1
Has abstractyes

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Same venueZenodo (CERN European Organization for Nuclear Research)→Same topicMilitary Technology and Strategies→French-language works237,207→