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Record W2085335014 · doi:10.5539/ass.v8n7p110

Literacy Encounters in a Non-Anglophone Context: Korean Study Abroad Students in a Malaysian Classroom

2012· article· en· W2085335014 on OpenAlexvenueno aff
Radha M.K. Nambiar, Noraini Ibrahim, Tamby Subhan Mohd Meerah

Bibliographic record

VenueAsian Social Science · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Education and Multiculturalism
Canadian institutionsnot available
FundersUniversiti Kebangsaan Malaysia
KeywordsLiteracyContext (archaeology)Study abroadPedagogyPsychologyPublic relationsSociologyMathematics educationPolitical scienceGeography

Abstract

fetched live from OpenAlex

The rapid proliferation of study abroad and transnational programmes have witnessed increasing student mobility within the Southeast Asian region that has thrust many learners into new learning environments and cultures. These students come with their own academic literacy practices built on experiences in their home countries and have to quickly shift to a new academic culture in the host country. This research highlights that it is erroneous to assume that academic culture and practices are the same across the region by investigating how Korean study abroad students are navigating their literacy practices in a Malaysian tertiary classroom. Data was collected using interviews, student literacy logs and researcher field notes over fourteen weeks. Findings show that these students were unprepared to deal with the new environment and resorted to literacy practices that they were comfortable with and useful in their home country’s academic culture. This implies that it is important to identify what is similar and different in the two learning environments to help study abroad students adjust and connect with their new learning context.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0080.004
Scholarly communication0.0060.004
Open science0.0010.006
Research integrity0.0010.003
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 head0.015
GPT teacher head0.388
Teacher spread0.374 · 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 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

Citations7
Published2012
Admission routes1
Has abstractyes

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