Repair Strategies for failed feature specification in Japanese: Evidence from loanwords, a reversing word game, and blending
Why this work is in the frame
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Bibliographic record
Abstract
This paper demonstrates repair strategies when place feature of the special moras in Japanese (the second half of a long vowel, moraic nasals, and the first half of a double consonant) fail to be specified in a usual manner. I posit three repair processes based on the observations of marked environments (loanwords, a word game called Sakasa Kotoba, blending): (i) over-application of regular structures in core lexicon, (ii) irregular structures that are produced through The Emergence of the Unmarked (TETU), and (iii) game-specific structures. I illustrate that even in marked environments, repair processes make outcome structures as unmarked as possible with these strategies. Based on the observations in the marked environments (mainly from Sakasa Kotoba), I further discuss the process of morification and underlying representations of special moras.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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