Learning in Harmonic Serialism and the necessity of a richer base
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper reassesses the hypothesis that early phonotactic learning of constraint-based grammars relies on the Identity Map – i.e. it uses observed surface forms as the inputs which cause errors and drive learning via constraint reranking. We argue that this approach's success is closely tied to Optimality Theory's fully parallel grammatical evaluation. In the constraint-based derivational framework of Harmonic Serialism (HS; McCarthy 2000, 2007 b ), reliance on observed surface forms as inputs can block the discovery of ‘hidden rankings’ between markedness constraints, preventing the learner from discovering a restrictive grammar. This paper illustrates the problem, using a pattern of positional vowel restrictions in Punu (Kwenzi Mikala 1980), and considers the role of various learning assumptions. We conclude that hidden rankings are a fundamental obstacle to restrictive error-driven learning in any HS-like framework, and that learning in such frameworks inevitably requires consideration of some unattested surface forms as inputs, even at the earliest learning stages.
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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 | 0.001 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it