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Record W2467733103 · doi:10.1075/jslp.2.1.05mul

Listening to learners’ voices

2016· article· en· W2467733103 on OpenAlexaboutno aff
Mareike Müller

Bibliographic record

VenueJournal of Second Language Pronunciation · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsPronunciationActive listeningGermanPsychologyPerspective (graphical)PerceptionIdentity (music)NarrativeStudy abroadMathematics educationPedagogyLinguisticsComputer scienceCommunication

Abstract

fetched live from OpenAlex

This article investigates learners’ perceptions on pronunciation learning in study-abroad contexts from a qualitative perspective. While previous research focused mainly on quantitative measurements of pronunciation gains with mixed results, this study takes a more learner-centered approach and examines the impact of socio-psychological factors on learning foreign pronunciation, which appears to be a highly individual and at times conflict-prone process with which sojourners are confronted. The study draws on the cases of five Canadian students who studied abroad at German universities for one or two semesters. The data collection involved a learning history questionnaire; semi-structured interviews pre-, mid-, and post-sojourn; and bi-weekly e-journals. The data was analyzed and interpreted within the framework of narrative analysis. The results show how sojourners’ beliefs about the importance of pronunciation, community participation, identity-related challenges, and obstacles to pronunciation learning influence and help explain individually different learning behaviors and results.

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)0.004
metaresearch head score (Gemma)0.017
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 score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0040.006
Scholarly communication0.0070.005
Open science0.0010.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0120.002

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 head0.032
GPT teacher head0.406
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

Citations6
Published2016
Admission routes1
Has abstractyes

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