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Record W4410088914 · doi:10.29057/lc.v6i12.14337

Cultivating Language Learning Through Cultural Sensitivity and Empathy. An e-Tandem Case Study of Mexican and Canadian Students

2025· article· en· W4410088914 on OpenAlexaboutno aff
Raquel Acosta Fuentes, Andrea A. Valdiri Suesca

Bibliographic record

VenueRevista Lengua y Cultura · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsEmpathyPsychologyTandemCultural sensitivitySensitivity (control systems)Culturally sensitiveSocial psychologyEngineeringPsychotherapist

Abstract

fetched live from OpenAlex

This article examines how cultural sensibility and empathy towards the interlocutor foster language learning and intercultural competence. We analyzed the results of an e-Tandem program conducted between a private high school in Vancouver, Canada, and a public high school in Mexico City. The program consisted of two phases: the first involved a series of online letter exchanges, as well as the exchange of handwritten letters and souvenirs; in the second, participants engaged in virtual meetings through a video conferencing platform. We collected data through surveys that captured challenges, language strategies, and cultural insights; video recordings of student interactions; facilitator observations; and student-created artifacts such as letters, postcards, and gifts. Despite the significant differences in the participants' backgrounds, a key finding of the study was that the process of writing letters, receiving and replying to them provided students with a valuable space for reflection. This allowed them to learn from others but, most importantly, to learn about themselves. We found that cultural sensitivity involves identifying differences, acknowledging similarities, and, above all, valuing one’s own culture. This foundation helps students negotiate intercultural encounters, know how to address topic they find challenging, and develop a genuine interest in others.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.067
Threshold uncertainty score0.935

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.033
GPT teacher head0.332
Teacher spread0.299 · 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 teacher head, 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

Citations0
Published2025
Admission routes1
Has abstractyes

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