Bibliographic record
Abstract
Archibald's article makes a strong case for abstract symbolic representations in the phonological grammars of second language learners/users (L2ers). The evidence he brings to bear on this comes principally from the prosodic domain. However, the case for abstractness is hardest to defend in the segmental domain, specifically when it comes to motivating a role for features. The goal of this commentary is to show that L2 speech perception is mediated, in part, by features and thereby provide support for Archibald's claim. Two speech perception studies are discussed. The first study shows that the status of the feature [nasal] in vowels in the first language (L1) grammar, as contrastive (French) or allophonic (English), impacts naive perception of novel nasal vowels. French listeners successfully perceive the novel vowels; English listeners' success is hindered by the phonological status of [nasal] in the L1 grammar. The results are proposed to support a role for abstract phonological representations: for features; for the conditions under which they must be shared across segments; and for a theory of licensing that can capture the licensing potential of different prosodic positions. The second study shows that the absence of the feature [SG] from the L1 grammar of French negatively impacts the ability to perceive and build an appropriate representation for English /h/. The lack of [SG] is proposed to account for three types of behaviour displayed by L2ers: their failure to perceive [h] as distinct from Ø; their VOT values for 'voiced' and 'voiceless' stops which fall between those of L1 French and those of target L2 English; and their overapplication of aspiration to stops in sC clusters. It is shown that these patterns cohere under a phonological account, as features have a classificatory function and are thereby expected to shape phonological behaviour across multiple groups of segments.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".