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.
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.
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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".