Syntagmatic distinctness in consonant deletion
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 article examines the role of distinctness between adjacent segments in consonant deletion. On the basis of five stop-deletion patterns, it establishes a correlation between the likelihood of cluster simplification and the level of similarity between the consonants in the cluster. This correlation is motivated on perceptual grounds, and an OT analysis of similarity avoidance is provided in which perceptual factors are integrated in the grammar through both faithfulness and markedness constraints. This perceptual approach improves in two ways on previous analyses, notably the OCP. First, it integrates similarity avoidance within a more general perception-based framework, which accounts naturally for its gradient nature. Second, it uncovers a distinction between absolute and contextual similarity avoidance between adjacent segments, depending on whether similarity avoidance is established without reference to the context in which the segments appear or relative to the quality of the perceptual cues available to the segments.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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