Waiting in the wings: The place of phonology in the study of multilingual grammars
Bibliographic record
Abstract
Certain properties of second language (L2) speech are well studied, yet it is uncontroversial to note that L n phonology (where n = any natural number; e.g. L2, L3, etc.) is under-represented in generative approaches to language acquisition compared with the domain of morphosyntax. If, however, we look at L n input and output without taking the learnability of abstract mental representation seriously in our psycholinguistic probes then we miss out on fundamental knowledge as to the nature of a multilingual grammar. L n knowers have complex, phonological grammars whose properties help us to describe and explain their knowledge and behaviour. Many approaches (e.g. usage-based; exemplar) have assumed that phonology can be learned by ‘noticing’ elements in the input. Such a view ignores Plato’s Problem of the acquisition of knowledge as well as the corollary of Orwell’s Problem. Phonology is rich, hierarchical, recursive, governed by UG (universal grammar), and subject to poverty-of-the-stimulus effects. Assigning phonetic tokens to phonological categories entails an algebraic function in which the phonological categories act as variables. Interestingly, this is related to the question of whether phonology is ‘merely’ a system of externalization (which implies it evolved after Merge) or whether there is evidence of it emerging earlier in the lineage of Homo sapiens . I present some arguments that human phonology is not just the linearization of syntax implemented by computationally-simpler, evolutionarily-older machinery. I discuss empirical data which demonstrate the utility of explaining multilingual phonological grammars with reference to hierarchical constituents at the levels of feature, syllable, foot, prosodic word, and phonological phrase; none of these structures are read off the input in a straightforward way. By recognizing the epistemological, representational, and learnability issues related to phonological knowledge (and its interfaces), we deepen our understanding of the full range of the cognitive architecture of the multilingual language faculty.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.020 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| 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".