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Record W2972335114 · doi:10.1075/pl.22001.kim

The efficacy of lexical stress diacritics on the English comprehensibility and accentedness of Korean speakers

2022· article· en· W2972335114 on OpenAlexaff
Keun Kim, John Archibald

Bibliographic record

VenuePedagogical Linguistics · 2022
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsStress (linguistics)Read aloudPsychologyLinguisticsAudiologyMedicineReading (process)

Abstract

fetched live from OpenAlex

Abstract The purpose of the study was to examine the efficacy of lexical stress diacritics on the English comprehensibility and accentedness of Korean speakers. To this end, 30 native Korean participants read aloud 15 English sentences without diacritics in the pretest. Then, they were given explicit instructions on the production of higher pitch and extended duration as a marker of English stress with musical notation provided. In the posttest, the participants read aloud the same sentences as were in the pretest but which had diacritics indicating stress placement. In the delayed posttest, two days after the pretest and the posttest, the participants read 15 sentences without diacritics again to see if the effects of the treatment were retained. Randomized speech samples were rated by three native speakers of English in relation to comprehensibility and accentedness. Findings showed that significant improvements were observed after the treatment in both comprehensibility and accentedness.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.149
GPT teacher head0.414
Teacher spread0.265 · 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 designBench or experimental
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
Published2022
Admission routes1
Has abstractyes

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