Using a contrastive hierarchy to formalize structural similarity as I-proximity in L3 phonology
Bibliographic record
Abstract
Abstract In this paper I argue that cross-linguistic similarity in third language acquisition is determined by a structural hierarchy of contrastive phonological features. Such an approach allows us formalize a predictive notion of I-proximity which also provides an explanatory model of L2, and L3 phonological knowledge (represented in an integrated I-grammar). The metrics of phonological similarity (i.e., structural not acoustic) are analogous to morphosyntactic similarity in that both morphosyntactic and phonological approaches can compare the outcomes of parsing the L3 input by the L1 hierarchy and by the L2 hierarchy. From this starting point I propose a conservative, incremental learning theory to guide subsequent reconstruction of the L3 grammar. Under this model, it can be argued that phonology is part of Faculty of Language Narrow (FLN). The (gradient) phonetic material comes from outside the FLN but the linguistic computational system converts it to discrete abstract elements that can be manipulated by the learner.
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How this classification was reachedexpand
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".