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Record W150157217 · doi:10.1075/lllt.26.06sch

Study abroad and its effect on speech act performance

2010· book-chapter· en· W150157217 on OpenAlexaboutno aff
Gila A. Schauer

Bibliographic record

VenueLanguage learning and language teaching · 2010
Typebook-chapter
Languageen
FieldArts and Humanities
TopicLanguage, Discourse, Communication Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsGermanVariety (cybernetics)Study abroadCompetence (human resources)Latin AmericansLinguisticsForeign languagePolitical sciencePsychologyHistoryPedagogyComputer scienceSocial psychologyLawArtificial intelligence

Abstract

fetched live from OpenAlex

Although the effect of the study abroad environment on foreign language learners’ speech act performance had been underexplored for many years, a number of studies have been published in the last decade that help to shed light on the impact of study abroad sojourns on language learners’ pragmatic competence. In this chapter, I will review and discuss investigations examining the effect of study abroad on language learners in a variety of study abroad contexts (e.g. Canada, United States of America, Latin America, France, Germany, Great Britain) and involving a variety of native and target language combinations (e.g. Chinese – English, English – French, English – German, English – Spanish, German – English, German – French, Japanese- English). The speech acts investigated are: advice, apologies, leave-taking, offers, refusals, requests and suggestions.

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.017
GPT teacher head0.299
Teacher spread0.282 · 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 designObservational
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

Citations18
Published2010
Admission routes1
Has abstractyes

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