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Record W2982702440 · doi:10.5430/ijhe.v8n7p34

The Correlation of Educational Standards for Museology (Ba)

2019· article· en· W2982702440 on OpenAlexvenueno aff
Olga A. Masalova, Makka I. Dolakova, Marina Mefodeva, Adelya Ilhamovna Sattarova

Bibliographic record

VenueInternational Journal of Higher Education · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicMuseums and Cultural Heritage
Canadian institutionsnot available
FundersKazan Federal University
KeywordsMuseologyField (mathematics)Relevance (law)Russian federationCurriculumProfessional standardsCultural heritageEngineering ethicsPolitical sciencePedagogySociologyEngineeringGeographyArchaeologyRegional scienceLaw

Abstract

fetched live from OpenAlex

This article is devoted to the problem of correlation of the higher education system of the Russian Federation in accordance with the requirements of professional standards. The relevance of this problem is due to a radical change in approaches to personnel Museum policy and in the profile system of higher education. The most controversial issue in this study is the fragmentary implementation of professional standards in the field of Museum business and the dependence of the educational process on them. The article reveals the problems of determining the qualification requirements for a number of Museum professions, and assessing the possibility of their solution at the level of the education system. The main method of research is the method of comparative analysis, which allowed to determine the content load of the definitions used and to correlate professional and educational standards in the study area. The materials of the article can be useful for the formation of working curricula in the field of museology and protection of cultural and natural heritage.

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)0.015
metaresearch head score (Gemma)0.077
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 score0.015
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.077
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.003
Science and technology studies0.0020.003
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.015
GPT teacher head0.308
Teacher spread0.293 · 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
Published2019
Admission routes1
Has abstractyes

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