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Record W55825087 · doi:10.5206/cie-eci.v41i3.9213

Reconceiving International Education: Theorizing Limits and Possibilities for Transcultural Learning

2013· article· en· W55825087 on OpenAlexaffvenue
Paul Tarc, Aparna Mishra-Tarc, Nicholas Ng-­A-­Fook, Roopa Desai Trilokekar

Bibliographic record

VenueComparative and International Education · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Education and Multiculturalism
Canadian institutionsUniversity of OttawaYork UniversityWestern University
Fundersnot available
KeywordsFraming (construction)MainstreamCurriculumSociologyInternational educationPedagogyEthnographyStudy abroadHigher educationEpistemologyAnthropologyPolitical science

Abstract

fetched live from OpenAlex

This multi-voiced paper explores the micro-level dimensions of human learning and becoming from transcultural encounters, lessons and/or curriculum under heightened transnationalism. It posits that mainstream approaches to conceptualizing the ‘education’ of international education lack sufficient theorization of difference, sociality, history and learning in trans-local spaces and suggests that there are expanding networks of transcultural engagements to be examined under the umbrella of international education. To explore this reconceived pedagogical landscape of international education three specific cases are presented: an auto-ethnographic reflection on coming into and making sense of one’s international experience, a conceptual framing of internationalizing preservice education curriculum and a qualitative analysis of the pedagogical impacts of undergraduates’ international internships. Each case illustrates the complexities, possibilities and challenges of (framing) learning and becoming in sites of transcultural engagement.

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)0.011
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0090.106
Scholarly communication0.0230.030
Open science0.0030.022
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0040.000

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 head0.111
GPT teacher head0.434
Teacher spread0.323 · 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 designTheoretical or conceptual
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

Citations11
Published2013
Admission routes2
Has abstractyes

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