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 paper first provides an overview of factors that constrain ultimate attainment in adult second language (L2) pronunciation, finding that first language influence and the quantity and quality of L2 phonetic input account for much of the variation in the degree of foreign accent found across adult L2 learners. The author then evaluates current approaches to computer assisted pronunciation training (CAPT), concluding that they are not well grounded in a current understanding of L2 accent. Finally, the author reports on a study in which twenty-two Mandarin speakers were trained to better discriminate ten Canadian English vowels. Using a specially designed computer application, learners were randomly presented with recordings of the target vowels in monosyllabic frames, produced by twenty native speakers. The learners responded by clicking on one of ten salient graphical images representing each vowel category and were given both visual and auditory feedback as to the accuracy of their selections. Pre- and post-tests of the learners’ English vowel pronunciation indicated that their vowel intelligibility significantly improved as a result of training, not only in the training context, but also in an untrained context. In a third context, vowel intelligibility did not improve.
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 | 0.000 | 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) | 0.004 | 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