Lexical aspects of comprehensibility and nativeness from the perspective of native-speaking English raters
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it