Bibliographic record
Abstract
In this article, I tackle the question of differential substitution in L2 phonology. A classic example of the phenomenon is learners from different L1s attempting to acquire the L2 English interdental fricative /θ/. Speakers of some languages (e.g., Japanese) tend to pronounce the /θ/ as [s] while speakers of other languages (e.g., Russian) tend to pronounce the /θ/ as [t]). Since both Japanese and Russian have both /s/ and /t/ in their phonemic inventories, it is interesting to ask why one language would choose [s] and the other [t]. What I argue in this article is that it is not a local comparison of two sounds, two features, or two phonemes that will determine why one segment rather than another is substituted. Rather, I argue that we must consider the formal representation of the entire segmental inventory (represented as a contrastive hierarchy) in order to understand why the Japanese pick the [s] but the Russian the [t] as the “best” substitute for the English /θ/. What I will demonstrate is that in the languages that substitute [s], [continuant] is the highest-ranked feature that has scope over the place and voice features in the contrastive hierarchy of phonological features. In the languages that substitute [t], the place and voice features rank above [continuant].
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".