Bibliographic record
Abstract
Abstract The main goal of this book is to probe questions about the nature of an interlanguage (IL) grammar (i.e. the grammar of a bilingual or multilingual). I approach these questions from a cognitive science perspective which draws upon abstract representational structures in demonstrating that phonological knowledge underlies the surface phonetic properties of L2 speech. Specifically, the book will demonstrate that IL grammars are not ‘impaired’, ‘fundamentally different’, or ‘shallow’ (as some have argued). The phonological grammars are complex, hierarchically structured mental representations that are governed by the principles of linguistic theory, including the principles of Universal Grammar. I craft a model which addresses Plato’s Problem (learning in the absence of evidence) and Orwell’s Problem (resistance to learning in the face of abundant evidence). Furthermore, the study of grammatical interfaces (phonetics/phonology; phonology/morphology; phonology/syntax) reveals the necessary design conditions for an internally consistent architecture for a comprehensive model of second language speech. The resulting empirically motivated model is parsimonious in accounting for all aspects of L2 speech from phonological feature, to segment, to word, to sentence. The book concludes with discussion of why phonology has been underrepresented in generative approaches to second language acquisition, as well as some of the implications of second language phonology for applied linguistics and language pedagogy.
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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 | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".