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Record W2982356528 · doi:10.5430/ijhe.v8n7p116

Socio-Cultural Competence in Teaching Foreign Languages

2019· article· en· W2982356528 on OpenAlexvenueno aff
Igor O. Guryanov, Alina E. Rakhimova, Marisol C. Guzman

Bibliographic record

VenueInternational Journal of Higher Education · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicForeign Language Teaching Methods
Canadian institutionsnot available
FundersKazan Federal University
KeywordsSociocultural evolutionForeign languageCultural competenceCompetence (human resources)Communicative competencePersonalityCultural knowledgePsychologyCultural communicationSociologyCultural diversityPedagogyLinguisticsSocial psychologyAnthropologyCommunication

Abstract

fetched live from OpenAlex

This article addresses the problem of integrating socio-cultural components into teaching and upbringing through foreign languages. The aim of teaching a foreign language means not only acquiring communication skill but also forming cultural and linguistic personality. The main aim of any communication is to be understood by interlocutor. The effectiveness of this process is directly dependent on the reached level of mutual understanding between communicants. To achieve this aim partners should have the willingness and capacity to form dialogue of cultures which presupposes the existence of a socio-cultural competence. Sociocultural competence includes knowledge about values, beliefs, behavior patterns, customs, traditions, language and cultural achievements peculiar to society. This competence occurs in the framework of socio-cultural education and training, i.e. in the process of personalizing the culture and national traditions of the studied language country.

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)0.006
metaresearch head score (Gemma)0.009
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 score0.006
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.004
Scholarly communication0.0040.002
Open science0.0000.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.019
GPT teacher head0.419
Teacher spread0.400 · 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

Citations15
Published2019
Admission routes1
Has abstractyes

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