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 investigates how Mandarin and Slavic language speakers’ comprehensibility, accentedness, and fluency ratings, as assigned by experienced teacher-raters and novice raters, align with discrete linguistic measures, and raters’ accounts of influences on their scoring. In addition to examining mean ratings in relation to rater experience and speaker first language background, we correlated ratings with segmental, prosodic, and temporal measures. Introspective reports were segmented, coded, enumerated, and submitted to loglinear analysis to elucidate influences on ratings. Results showed that ratings were strongly correlated with prosodic goodness and moderately correlated with segmental errors, implying the importance of both segmentals and prosody in L2 speech ratings. Experienced teacherraters provided lengthier reports than novice raters, producing more comments for all coded categories where an error was identified except for pausing (a disfluency marker). This may be because novice raters observed little else about the speech or struggled to pinpoint or articulate other features.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.151 | 0.004 |
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