Shaping phonetic performance in second language learners
Bibliographic record
Abstract
This study aimed to evaluate the efficacy of a software-administered shaping procedure in guiding English monolinguals to acquire accurate Mandarin pronunciation. A single-subject reversal ABAB design was used to evaluate treatment effects. A purposely-developed algorithm generated an accuracy score defined as the similarity between a participant’s utterance and the target pronunciation. The shaping procedure provided performance-dependent reinforcement, while the control condition provided performance-independent reinforcement at a density yoked to the shaping procedure. A no-feedback condition assessed spontaneous language learning ability prior to treatment. Data were evaluated via visual analysis and complemented with effect size analyses and repeated-measures ANOVAs. There were no overall treatment effects. However, three individuals demonstrated a statistically significant difference between treatment and control. A follow-up study compared shaping to no feedback using a simplified procedure and simpler stimuli. A multiple-baseline design was used. The results showed no treatment effects. Possible contributing factors and directions for future research are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".