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Record W1847068987 · doi:10.18806/tesl.v19i1.920

Developing Cross-Cultural Awareness: Learning Through the Experiences of Others

2001· article· en· W1847068987 on OpenAlexvenueno aff
Garold L. Murray, Deborah J. Bollinger

Bibliographic record

VenueTESL Canada Journal · 2001
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsSyllabusPedagogyEthnographyPsychologyCross-culturalCultural competenceProcess (computing)Foreign languageSociologyLinguisticsComputer scienceAnthropology

Abstract

fetched live from OpenAlex

This article offers communicative activities designed to enhance the cross-cultural awareness of Japanese university students whose language levels range from beginner to intermediate. Facilitating the development of cross-cultural awareness of foreign language students who have never lived in another culture or even visited one can be problematic. Although many educators have responded to the challenge with a knowledge-based approach, a recent study suggests a syllabus that emphasizes constructivist, process-oriented tasks would be more effective. In their efforts to implement the latter approach, the authors have devised activities that range from student-generated interviews of a guest speaker and e-mail exchanges with target language speakers to amini-video ethnography project that focuses on the cross-cultural experiences of others. The article outlines these activities and concludes with a brief evaluation of their effectiveness based on the learners' reactions.

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)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0030.005
Scholarly communication0.0060.008
Open science0.0010.010
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.069
GPT teacher head0.317
Teacher spread0.247 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations19
Published2001
Admission routes1
Has abstractyes

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Same venueTESL Canada JournalSame topicEFL/ESL Teaching and LearningFrench-language works237,207