Second Language Sentence Stress Assignment: Self‐ and Other‐Assessment
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".