Bibliographic record
Abstract
Listeners are remarkably sensitive to non-native patterns of speech, whether they are presented with full sentence productions, or with very short or severely degraded speech, including noisy, filtered, and temporally disrupted utterances. Furthermore, even phonetically unsophisticated listeners can reliably scale accents. From the standpoint of second language (L2) users, speaking with a detectible accent has important social consequences. One is that L2 speech is sometimes less intelligible or may require listeners to allocate more processing resources than does native speech. However, evidence also indicates that some salient phonological markers of L2 status have little or no impact on listener comprehension. Distinguishing these from accent features that reduce intelligibility is a critical concern in language pedagogy. Another consequence noted in empirical research is that accent stereotyping and negative social evaluation of accented speakers are linked to discrimination in remuneration, employment, and services. They may also be implicated in the exploitation of L2 speakers through questionable claims about the value of “accent reduction.” This synthesis of previous and new research findings highlights a number of issues concerning methodology and interpretation in L2 speech research that are relevant to language teaching and assessment, and to human rights litigation. [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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".