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Record W7036389992

The audio-recorder as a resource for L2 learning in study abroad

2023· article· en· W7036389992 on OpenAlexaboutno aff

Bibliographic record

VenueScholarSpace (University of Hawaii at Manoa) · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Electron Microscopy Techniques and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsStudy abroadGermanResource (disambiguation)Mobile deviceInformal learningLanguage acquisitionControl (management)
DOInot available

Abstract

fetched live from OpenAlex

Research on second language (L2) learning in study abroad (SA) often includes close analyses of audio or video recordings of interactions among speakers in the host country (Diao, 2022; Mitchell, 2023). These recordings are increasingly being collected by SA students themselves using handheld and/or mobile devices. Given that participants are aware and often in control of the recording process, some researchers (e.g., Gordon, 2013; Speer & Hutchby, 2003) have suggested that scholars should attend to how participants orient to the recorder as a resource for doing things (i.e., interactive and relational work) in talk. We take up these researchers’ call by examining participant-collected recordings of peer talk from two SA case studies: American learners of German in Germany and Japanese learners of English in Canada. Analyses reveal that the audio-recorder afforded participants additional opportunities to collaboratively do research, build informal relationships, and practice language while abroad. Our results highlight how integrating recording devices in SA can support those seeking more informal ways to practice the L2 with peers in 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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.920
Threshold uncertainty score0.411

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.007
GPT teacher head0.303
Teacher spread0.297 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations1
Published2023
Admission routes1
Has abstractyes

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