Bibliographic record
Abstract
In studies of L2 speech learning it is common to draw conclusions (sometimes as an afterthought) about the potential pedagogical value of research findings, particularly when they provide insights into specific pronunciation difficulties faced by L2 learners or when they indicate improved perception or production as a result of laboratory training. However, many important research findings have, as yet, had only a minor impact on second language teaching, and there remains a significant gap between what has been empirically established in the research laboratory and what is actually practiced in the classroom. This presentation will identify some of the reasons for this disparity through an examination of goals and priorities that are commonly accepted by pedagogical specialists. In particular, it will be argued that the implementation of research findings depends on establishing practical ways of improving L2 learners’ speech intelligibility (as opposed to mere accent reduction) for a diverse audience of interlocutors in authentic interactive settings. In general, there is a need for research that is specifically motivated by pedagogical concerns and that is informed by an awareness of a wide range of current issues in applied linguistics. [Research supported by SSHRC.]
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.
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 | 0.036 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.038 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".