Bibliographic record
Abstract
This article addresses the issue of teaching pronunciation in English as a second language (ESL) classes by specifically looking at the impact of teaching lexical stress rules and tendencies on learners' stress placement performance. Sixteen rules in the form of interactive worksheets were taught in three ESL classes at pre‐intermediate, intermediate, and upper intermediate levels (N = 38). The rules were taught and reviewed during 9 weeks, each taking approximately 25 minutes of class time. They dealt with four areas: word categories, compound nouns, verb‐noun pairs, and suffixes. The participants recorded a list of carefully chosen 100 words two times, once before and once after the teaching of the rules. The results show a statistically significant reduction of mean error percentage from 33.8% to 18.3%, with an effect size (Cohen's d) of 1.67. The implications of this research are twofold. On the one hand, it is evidence for the successful teaching of suprasegmentals and in particular lexical stress rules in ESL classes, and on the other, it contains a methodology and a sample lesson plan for teaching such rules (see Appendix A ). The article thus argues for the inclusion of English lexical stress prediction rules in the ESL pronunciation curriculum to enhance learners' overall intelligibility.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".