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.
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.
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 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 | 0.001 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it