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Record W2982471687 · doi:10.5430/ijhe.v8n7p8

Investigation in the Interethnic Relations in the Republic of Tatarstan: Teaching Methods of the Adaptation of the Local Population to the Presence of Migrants

2019· article· en· W2982471687 on OpenAlexvenueno aff
Т.А. Титова, Elena V. Frolova, Elena Gushchina, Bulat Ildarovich Fakhrutdinov

Bibliographic record

VenueInternational Journal of Higher Education · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicCultural, Linguistic, Economic Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEthnic groupThe RepublicTatarHuman settlementRussian federationPopulationConfessionalPoliticsRelevance (law)Settlement (finance)SociologyPolitical scienceAdaptation (eye)GeographyRegional scienceAnthropologyDemographyLawPsychologyEpistemology

Abstract

fetched live from OpenAlex

The relevance of the research is determined by the problem of interethnic relations in the multi-ethnic and multi-confessional regions of the Russian Federation. The purpose of the article is to describe the attitude of the local population to wards migrants of different cultures in their settlements in the Republic of Tatarstan in 2017. The leading approach in studying this problem is multi paradigmatic methodology. The article incorporate the attitude of the local population to the presence of migrants in the Republic of Tatarstan in 2014-2017, provides the comparative data on major cities and towns of the Republic, helps grasping the issue. The authors paid special attention to the general level of pupils. The results of the article can be useful for ethnologists, social and cultural anthropologists, political scientists, as well as for the representatives of the bodies responsible for the inter-ethnic interaction.

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)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.003
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.001
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 head0.076
GPT teacher head0.417
Teacher spread0.341 · 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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