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Record W2340995446 · doi:10.17169/fqs-17.2.2387

Lasting Impact of Study Abroad Experiences: A Collaborative Autoethnography

2015· article· en· W2340995446 on OpenAlexaff
Jordana Garbati, Nathalie Rothschild

Bibliographic record

VenueForum: Qualitative Social Research (Freie Universität Berlin) · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsConcordia UniversityWilfrid Laurier University
Fundersnot available
KeywordsAutoethnographyStudy abroadGrounded theoryMulticulturalismIdentity (music)Narrative inquiryNarrativeSociologyPedagogyQualitative researchLinguisticsGender studiesSocial scienceAestheticsArt

Abstract

fetched live from OpenAlex

Researchers in the field of study abroad have focused on language, identity construction, and motivation, yet few studies have shown its lasting impact on participants. This article contains the reflections of two individuals who took part in studies abroad and remain engaged in multicultural education and in the instruction and research of second language acquisition. We review the literature in the area of study abroad, then discuss the suitability of using a collaborative autoethnography (CAE) approach, defined as "the study of self collectively" (CHANG, WAMBURA NGUNJIRI & HERNANDEZ, 2013, p.11) for this project. We analyzed our data, which are in the form of reflective narratives and archived e-mails, through open coding, based on grounded theory methodology (see CORBIN & STRAUSS, 2015). Four major themes surfaced from our data analysis: language and culture; academics; identity; and lasting impact. Finally, we compare our experiences, identify some of the lasting effects of our time abroad, and consider both the practical and theoretical implications of the research. This research has been useful for us to understand CAE and the lasting effects of study abroad experiences on students who become language teachers. URN: http://nbn-resolving.de/urn:nbn:de:0114-fqs1602238

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.076
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.325
GPT teacher head0.524
Teacher spread0.199 · 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.

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

Citations16
Published2015
Admission routes1
Has abstractyes

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