MétaCan
Menu
Back to cohort

Social communications of students in the modern intercultural space

2021· article· en· W3174125165 on OpenAlexaboutno aff
Taras Kuzmenko, Тетяна Цой, Iuliana Goncharenko, Liudmyla ZHVANIA, Nataliia Kvitko

Bibliographic record

VenueLAPLAGE EM REVISTA · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Social Development in Ukraine
Canadian institutionsnot available
Fundersnot available
KeywordsHigher educationInstitutionSociologyRepresentation (politics)Space (punctuation)Study abroadSocial spaceIntercultural communicationMedia studiesPolitical sciencePedagogyLibrary scienceSocial sciencePoliticsLaw

Abstract

fetched live from OpenAlex

The aim of this article is to study the influence of social communications on the formation of relations between students in the intercultural space on the example of the State Higher Educational Institution "Vasyl Stefanyk Precarpathian National University" and Kyiv University named after Borys Hrinchenko. Methods: analysis, synthesis, abstraction, modeling, description, observation, comparison, tabular and graphical representation, questionnaires and generalizations. Results: It is determined that countries such as Australia, Canada, Great Britain, New Zealand, France and the Netherlands have the highest rates of attracting foreign students to study in higher education institutions. The most international universities in the world are the University of Hong Kong, ETH Zurich, Chinese University of Hong Kong, University of Oxford and Imperial College London, which occupy the first five positions in the World University Rankings 2021. It was found that most often social communication between students belonging to different socio-cultural groups occurs using social media.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.003
Scholarly communication0.0040.002
Open science0.0000.003
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 head0.069
GPT teacher head0.419
Teacher spread0.350 · 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 designQualitative
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
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueLAPLAGE EM REVISTASame topicEducation and Social Development in UkraineFrench-language works237,207