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Record W3189407662 · doi:10.32996/ijllt.2021.4.7.4

A Qualitative Investigation on the Role of Social Media in Intercultural Communication Competence: The Moroccan Diaspora in Canada as a Case-Study

2021· article· en· W3189407662 on OpenAlexaboutno aff
Fatima Zahraa Boutabssil

Bibliographic record

VenueInternational Journal of Linguistics Literature & Translation · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsSocial mediaIntercultural communicationCompetence (human resources)Social competencePsychologyExploratory researchQualitative researchDiasporaIntercultural competenceSociologySocial psychologyPedagogyPolitical scienceSocial changeGender studiesSocial science

Abstract

fetched live from OpenAlex

This paper aims to investigate how Moroccan migrants established in Canada utilize social media to improve their intercultural communication competence. It employs the exploratory sequential design based on the use of semi-structured interviews followed by a questionnaire. The results show that before migration, social media played an important role in providing the migrants with preliminary knowledge about the host culture via YouTube. Social media also facilitated communication with Canadians via Facebook. After migration, social and direct interaction was proven to be more effective in developing the participants’ ICC. As such, social media only played an informative role, whereas much of the participants’ intercultural knowledge, skills, attitudes, and awareness were developed from face-to-face communication. The study concludes that no matter how important social media can become, improving intercultural communication competencies cannot take place independently from face-to-face 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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.482
Threshold uncertainty score0.969

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0200.008
Scholarly communication0.0040.001
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 head0.062
GPT teacher head0.373
Teacher spread0.311 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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Same venueInternational Journal of Linguistics Literature & TranslationSame topicInternational Student and Expatriate ChallengesFrench-language works237,207