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Record W4404042294 · doi:10.1111/lang.12682

Second Language Sentence Stress Assignment: Self‐ and Other‐Assessment

2024· article· en· W4404042294 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueLanguage Learning · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsSimon Fraser UniversityConcordia UniversityUniversity of Calgary
Fundersnot available
KeywordsPsychologyLinguisticsSentenceStress (linguistics)

Abstract

fetched live from OpenAlex

Abstract Research on second language (L2) pronunciation self‐assessment reports a general misalignment between self‐ and other‐assessment. This has been attributed to the object of self‐assessment, the self‐assessment task, the measures to which self‐assessment is compared, and speakers’ characteristics. Here, we examined self‐assessment of a discrete phonological feature—sentence stress—by L2 English speakers as compared to the assessment of first language English listeners through a timed, forced‐choice judgment task with low‐pass filtered stimuli, which contained only suprasegmental cues. Additionally, we explored how individual differences among speakers predict self‐assessment. Speakers generally overestimated their accuracy in sentence stress assignment in a pattern resembling the Dunning‐Kruger effect despite the controlled nature of the task. Speakers with larger vocabulary size judged their sentence stress assignment as correct more often and showed greater overconfidence and miscalibration. Finally, the assessments of speakers with a background in applied linguistics and/or language teaching were more aligned with listeners’ assessments.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.725
Threshold uncertainty score0.995

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 studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.012
GPT teacher head0.270
Teacher spread0.258 · 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