Bibliographic record
Abstract
I present LIBPHON, a nonparametric regression-based model of phonological acquisition that induces a generalised and productive pattern of vowel harmony—including opaque and transparent neutrality—on the basis of simplified formant data. The model quickly learns to generate harmonically correct morphologically complex forms to which it has not been exposed. 1 Explaining phonological patterns How do infants learn the phonetic categories and phonotactic patterns of their native languages? How strong are the biases that learners bring to the task of phonological acquistion? Phonologists from the rationalist tradition that dominated the past half-century of linguistic research typically posit strong biases in acquisition, with language learners using innatelygiven, domain-specific representations (Chomsky and Halle, 1968), constraints (Prince and Smolensky, 2004) and learning algorithms (Tesar and rules or constraint rankings from which they can classify or produce novel instances. In the last decade, however, there has been a shift toward empiricist approaches to phonological acquisition, use and knowledge. In this literature, eager learning algorithms (Aha, 1997), in which training data are used to update intensional representations of functions or categories then discarded, have been the norm. 1 However, research in related fields—particularly speech perception— indicates that speakers ’ knowledge and use of language, both in production and comprehension, is at least partly episodic, or instance-based
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".