Systematic Irregularity in Japanese<i>Rendaku</i>: How the grammar mediates patterned lexical exceptions
Bibliographic record
Abstract
Abstract Exceptions to Japaneserendakuvoicing that are independent of Lyman’s Law have usually been considered to be random and unsystematic. This article proposes that such exceptions are largely systematic and can be explained through lexical specification and Positional Markedness. Two main types of systematicity are examined: the clustering of blocking cases around particular lexical items, and a prosodic size effect, where “long” compounds, with at least one constituent exceeding two moras, will disable blocking under most conditions. Lexical clustering is explained through lexical specification of features under Combinatorial Underspecification while the prosodic size effect is seen as an expression of Positional Markedness. It is argued that only in long compounds is the morpheme boundary at the edge of a Prosodic Word, a prosodically strong position that more freely permits the marked [−sonorant, +voice] featural combination ofrendakuvoicing to occur.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".