Lexical aspects of comprehensibility and nativeness from the perspective of native-speaking English raters
Bibliographic record
Abstract
Abstract This study analyzed the contribution of lexical factors to native-speaking raters’ assessments of comprehensibility and nativeness in second language (L2) speech. Using transcribed samples to reduce non-lexical sources of bias, 10 naïve L1 English raters evaluated speech samples from 97 L2 English learners across two tasks (picture description and TOEFL integrated). Subsequently, the 194 transcripts were analyzed through statistical software (e.g., Coh-metrix, VocabProfile) for 29 variables spanning various lexical dimensions. For the picture description task, separation in lexical correlates of the two constructs was found, with distinct lexical measures tied to comprehensibility and nativeness. In the TOEFL integrated task, comprehensibility and nativeness were largely indistinguishable, with identical sets of lexical variables, covering dimensions of diversity and range. Findings are discussed in relation to the acquisition, assessment, and teaching of lexical properties in L2 speech.
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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 | 0.006 | 0.029 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".