The foundations of accent and intelligibility in pronunciation research
Bibliographic record
Abstract
Our goal in developing this timeline was to trace the empirical bases of current approaches to L2 pronunciation teaching, with particular attention to the concepts of accent and intelligibility . The process of identifying suitable works for inclusion challenged us in several ways. First, the number of empirical studies of pronunciation instruction is far too small to provide an interesting perspective on the issues. In fact, the dearth of such investigations has been noted many times since at least as far back as the 1960s (Strain 1963; Sisson 1970). Consequently, tracing the roots of contemporary teaching practices required that we expand our purview to consider theoretically-motivated research, as well as influential non-empirical writing about pronunciation. Here we encountered a second problem: the field of applied phonetics and phonology is so diverse that it was very difficult to decide what to omit. The research follows multifarious threads, some of which can directly inform classroom practices, while others are more concerned with general learning influences and processes. In addition, a large body of speculative and opinionated commentary on pronunciation has been published, much of which has never been submitted to empirical test.
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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 | 0.019 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.003 | 0.047 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".