Deriving the Feature-Filling/Feature-Changing Contrast: An Application to Hungarian Vowel Harmony
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
The article explores an alternative to the interpretive procedure adopted in SPE and proposes a unified interpretive procedure for all languages. The proposal solves long-standing problems by making it unnecessary to refer to a third value of binary features [θF], to introduce negation into lexical representations (e.g., [NOT + rd]), or to introduce a feature filling/feature changing diacritic on rules.The article provides a metric for comparing extensionally equivalent rule systems and argues that the most concise formulation is not always the correct one, by appealing to crosslinguistic evidence.The proposal is illustrated by application to the target/trigger relations in Hungarian vowel harmony.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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