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Record W3082212896 · doi:10.1075/jslp.20038.mun

Foreign accent, comprehensibility and intelligibility, redux

2020· article· en· W3082212896 on OpenAlexaff
Murray J. Munro, Tracey M. Derwing

Bibliographic record

VenueJournal of Second Language Pronunciation · 2020
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsUniversity of AlbertaSimon Fraser University
Fundersnot available
KeywordsIntelligibility (philosophy)PronunciationLinguisticsReduxStress (linguistics)PsychologyComputer scienceNatural language processingCognitive psychologyEpistemologyPhilosophy

Abstract

fetched live from OpenAlex

Abstract We revisit Munro and Derwing (1995a) , providing retrospective commentary on our original methods and findings. Using what are now well-established assessment techniques, the study examined the interrelationships among accentedness, comprehensibility, and intelligibility in the speech of second-language learners. The key finding was that the dimensions at issue are related, but partially independent. Of particular note was our observation that speech can be heavily accented but highly intelligible. To provide a fresh perspective on the original data we report a few new analyses, including more up-to-date statistical modeling. Throughout the original text we intersperse insights we have gained over the past 25 years. We conclude with retrospective interpretations, including thoughts on the relevance of the study to contemporary second language teaching and especially pronunciation instruction.

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)0.005
metaresearch head score (Gemma)0.031
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.031
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.054
GPT teacher head0.355
Teacher spread0.301 · 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

Citations75
Published2020
Admission routes1
Has abstractyes

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