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
This article seeks to illuminate the degree of position-based variation observed in the acquisition of new segments in a second language and to explain such variability as the consequence of phonetic constraints; this approach contrasts with much previous research that has used typological markedness to the same end. Specifically, it is proposed that learners will have the least difficulty acquiring sounds that involve novel combinations of voicing and manner in positions that favor the phonetic implementation of these sounds. Moreover, on the assumption that not all parameters can be mastered simultaneously, it is predicted that learners will first acquire aspects of a segment's articulation that are perceptually salient and articulatorily easier. The data come from a study of the acquisition of French by 20 intermediate- and advanced-proficiency English-speaking learners of French. Acoustic analysis of the data reveals asymmetries that favor accuracy with manner in onsets versus more targetlike realization of voicing in codas, in which devoicing exists in the input. Beyond demonstrating the role of phonetic principles in determining position-based variation, the findings contribute to our understanding of the acquisition of new consonantal contrasts by providing empirical evidence from a non-Germanic language to bear on this line of inquiry.
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 | 0.001 | 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 | 0.000 | 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