Variation patterns in across-word regressive assimilation in Picard: An Optimality Theoretic account
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
Before the advent of Optimality Theory (OT), quantitative variation patterns were usually regarded as the outcome of a selection between categorical grammars (see Bailey, 1973; Bickerton, 1973, among others). In a constraint-based approach, like OT, one is able to account for variation without resorting to a separate grammar for each variant, since the framework allows for variation to be encoded in (and therefore predicted by) a single grammar, through variable ranking (or crucial nonranking) of constraints. Along the lines of Reynolds (1994) and Anttila (1997), this study supports the view that, from the predictions determined by a language-specific set of variably ranked constraints, it is possible to establish quantitatively the probability of application of each variant inherent to the variation process. As a consequence, the analysis of across-word regressive assimilation in Picard attempts to incorporate into the grammar of the language both abstract knowledge and quantitative patterns of language use.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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