Bibliographic record
Abstract
One of the major tenets of the Speech Learning Model (SLM) is that “if two L2 sounds differ in perceived dissimilarity from the closest sound in the L1 inventory, the more dissimilar of the L2 sounds will manifest the greater amount of learning” (Aoyama et al. 2004:248). Given that certain studies have provided “evidence of greater learning for [®] than [l] by N[ative]J[apanese] learners of English” (2004:246), the SLM hypothesis can only be upheld if English [l] is more similar to Japanese [R] than English [®] is. However, this is clearly counterintuitive since, by most accounts, [R] represents a central flap, [l] a lateral approximant, and [®] a central approximant. In this study, it will be argued that English laterals cannot be more similar to Japanese /r/ than English rhotics are, as the SLM would have it, unless the Japanese sound contains a lateral component such as that which is found in the flap [‰]. As it happens, a number of phoneticians and phonologists have argued that this is indeed the case with Japanese /r/, as will be shown, and this is something that the proponents of the SLM would need to acknowledge if their theoretical stance is to be maintained.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".