Lexical correlates of comprehensibility versus accentedness in second language speech
Bibliographic record
Abstract
The current project investigated the extent to which several lexical aspects of second language (L2) speech – appropriateness, fluency, variation, sophistication, abstractness, sense relations – interact to influence native speakers’ judgements of comprehensibility (ease of understanding) and accentedness (linguistic nativelikeness). Extemporaneous speech elicited from 40 French speakers of English with varied L2 proficiency levels was first evaluated by 10 native-speaking raters for comprehensibility and accentedness. Subsequently, the dataset was transcribed and analyzed for 12 lexical factors. Various lexical properties of L2 speech were found to be associated with L2 comprehensibility, and especially lexical accuracy (lemma appropriateness) and complexity (polysemy), indicating that these lexical variables are associated with successful L2 communication. In contrast, native speakers’ accent judgements seemed to be linked to surface-level details of lexical content (abstractness) and form (variation, morphological accuracy) rather than to its conceptual and contextual details (e.g., lemma appropriateness, polysemy).
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".