Phonological features and phonetic variation in multilingual grammars
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 Drawing on Archibald (2022a , b ), the chapter shows how a contrastive hierarchy model of segmental phonology can provide formal model of determining cross-linguistic similarity. Looking primarily at Arabic-French learners of English, the L1 and L2 features (including the markedness value of the feature) and the rankings influence L3 acquisition. Neurolinguistic and sociolinguistic evidence for the differential behaviour of marked versus unmarked values are discussed, and then it is shown how this variation can act as a cue for the learner to discover the L3 grammatical hierarchy. The author explores a theory of L3 restructuring based on principles of merger, redeployment, and triggering. Ultimately, it is argued that the learner compares the L1 and L2 contrastive hierarchy parses of the L3 input and chooses the one which is optimal for the L3 grammar.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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