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Record W4306839904 · doi:10.32674/jcihe.v14i4.4280

“Lock Us in a Room Together”

2022· article· en· W4306839904 on OpenAlexaffabout
Vander Tavares

Bibliographic record

VenueJournal of Comparative & International Higher Education · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsYork University
Fundersnot available
KeywordsSocializationMultilingualismMulticulturalismPedagogyDiversity (politics)Intercultural relationsLocal languagePerceptionSociologyCultural diversityIntercultural communicationPsychologySocial scienceAnthropology

Abstract

fetched live from OpenAlex

In Canada, research has examined international-local student interaction by focusing on international students’ perceptions and experiences. As such, the perspectives of local students toward socialization with international students remain less explored. Designed as a survey-based case study with 17 local students at a university in Ontario, this study sought to understand the perspectives of local students on how to improve socialization between the two groups. Additionally, this study investigated how local students conceptualized their experiences of multiculturalism and multilingualism at their university, considering the importance of diversity for the development of intercultural knowledge and intercultural relationships. Findings suggest that local students considered their university to be multicultural/multilingual primarily based on the availability of cultural events and different languages being spoken on campus. Additionally, local students ascribed much importance to socialization with international students, but expected the university to assume a more formal role in developing structured opportunities for the two groups to come together.

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: none
Teacher disagreement score0.208
Threshold uncertainty score0.413

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0260.014
Scholarly communication0.0050.004
Open science0.0010.011
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0390.010

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 head0.063
GPT teacher head0.418
Teacher spread0.355 · 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

Citations2
Published2022
Admission routes2
Has abstractyes

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