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Record W2474944757 · doi:10.36366/frontiers.v27i1.376

Beliefs about Language Learning in Study Abroad: Advocating for a Language Ideology Approach

2016· article· en· W2474944757 on OpenAlexafffund
Victoria Surtees

Bibliographic record

VenueFrontiers The Interdisciplinary Journal of Study Abroad · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsIdeologyAffordanceStudy abroadPerspective (graphical)Order (exchange)Value (mathematics)Language acquisitionSociologyOn LanguageLanguage educationPedagogyPsychologySocial psychologyEpistemologyLinguisticsMathematics educationCognitive psychologyPolitical scienceComputer scienceLawPolitics

Abstract

fetched live from OpenAlex

Governments, institutions, and students alike have a number of assumptions about the inherent value of the study abroad for language learning (Allen & Dupuy, 2012; Twombly, Salisbury, Tumanut, & Klute, 2012). To date the study abroad literature has conceptualized these assumptions as student-internal beliefs, motivations, perspectives and expectations. This paper proposes a language ideologies perspective as alternative to these learner-centred constructs in order to better recognize students’ beliefs and practices as socially and historically constituted. This paper reviews the main findings from beliefs-focused study abroad research before turning to the theoretical literature on language ideologies. Using illustrative studies to examine the affordances of a language ideology framework, I consider how notions of language ideology might provide new avenues for explaining how expectations become established resources for interpreting the study abroad experience.

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)0.012
metaresearch head score (Gemma)0.013
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 score0.012
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.025
Scholarly communication0.0120.008
Open science0.0010.008
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.308
Teacher spread0.295 · 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

Citations26
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueFrontiers The Interdisciplinary Journal of Study AbroadSame topicSecond Language Learning and TeachingFrench-language works237,207