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Record W2966864659 · doi:10.22329/jtl.v12i2.4927

“It Would Be Better If You Can Hang Out With Different People”: An Examination of Cross-National Interaction in Postsecondary Classrooms

2018· article· en· W2966864659 on OpenAlexvenueno aff
Christopher Johnstone, Diana Yefanova, Gayle A. Woodruff, Mary Montgomery, Barbara Kappler

Bibliographic record

VenueJournal of Teaching and Learning · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsValue (mathematics)PsychologyCross-culturalPedagogyInternational educationSociologyMedical educationPolitical scienceHigher educationMedicineLaw

Abstract

fetched live from OpenAlex

This study examines the motivations and experiences of international and domestic students on three U.S. campuses related to cross-national interactions within classroom settings. The study also examines the role of instructors in facilitating such interactions through individual and group interviews. Findings indicate that domestic students appreciate the global perspectives of international students related to course content. International students, in turn, appreciate the “real world” perspectives that domestic students provide about the US (but do not necessarily find value in their content-related comments). The implications of this study are that cross-national interactions have different meanings for different stakeholders (i.e., some perceive to benefit academically while others perceive to benefit culturally). The implications of this study relate to how instructors structure student interactions and what might be reasonable outcomes for students in international groups in postsecondary classrooms.

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)0.010
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 score0.013
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0130.006
Scholarly communication0.0060.004
Open science0.0010.009
Research integrity0.0010.003
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 head0.038
GPT teacher head0.367
Teacher spread0.329 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

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