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Record W2558039434 · doi:10.5206/cie-eci.v45i2.9291

Interdisciplinary Study Abroad as Experiential Learning

2016· article· en· W2558039434 on OpenAlexaffvenue
Jean Todd Stephenson Wilson, Rachel Brain, Erik Brown, Leila Gaind, Kaila Radan, Julia Redmond

Bibliographic record

VenueComparative and International Education · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsMcMaster University
Fundersnot available
KeywordsExperiential learningStudy abroadInstitutionExperiential educationContext (archaeology)InternationalizationService-learningPsychologyIdeal (ethics)Higher educationPublic relationsInterimPedagogySociologyPolitical scienceBusinessSocial scienceLawHistory

Abstract

fetched live from OpenAlex

Abstract Although study abroad would appear to be an ideal context for the learning through doing and reflecting that constitutes experiential education, if it fails to be rigorously approached as experiential learning, it not only falls short of its potential, but also risks reinforcing rather than confounding consumerist assumptions and behaviours in education. Co-authored by five former academic exchange participants and their professor/program director (who had remained at the home university), the paper explores the need and various possibilities for programming that would pay more than lip service to the idea of international study as experiential learning. Facilitation of ongoing critical reflection and meaningful connections among students returning from study abroad, those arriving from elsewhere, and those at the home institution who had not studied abroad presents itself as a significant post-sojourn opportunity, with the potential to contribute to the transformation and internationalization of the institution itself.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.215
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.083
GPT teacher head0.489
Teacher spread0.406 · 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

Citations9
Published2016
Admission routes2
Has abstractyes

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