Variation patterns in across-word regressive assimilation in Picard: An Optimality Theoretic account
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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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".