Bibliographic record
Abstract
Summary One of the most interesting problems in second-language (L2) phonology is how to account for differential substitution. This is the phenomenon by which speakers who lack a certain segment (sequence) in their first language (L1) may adopt alternative language-specific replacement strategies in the L2 they are attempting to acquire. It has recently been claimed by Weinberger (1997) that the reason why, for example, Japanese learners of English systematically replace English /θ ð/ by /s z/ while their Russian counterparts always substitute /t d/ is that fricatives are unspecified for the feature [continuant] in Japanese while in Russian, the stops constitute the default obstruents. What is argued here is that this analysis in terms of Underspecification Theory cannot possibly work in the case of European and Canadian French which evince an equally systematic differential substitution of /θ ð/ to /s z/ and /t d/ respectively even though they have an identical system of underlying obstuents. It is also suggested that a perception-based approach to the thorny problem of differential substitution would appear to be a much more promising avenue of research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".