Bibliographic record
Abstract
Introduction Optimality Theory (OT; Prince and Smolensky 2004) is a radical departure from the derivational model of previous versions of generative phonology. Any new theory puts old questions into a new light, and it is illuminating to explore the relationship between OT and the Contrastivist Hypothesis. I will show that the contrastive hierarchy, being a static set of conditions, lends itself very easily to formulation in terms of a set of constraints, and hence to OT. At the same time, I will argue that OT is not itself a theory of contrast, but is capable of instantiating a wide range of such theories. The relationship between OT and the Contrastivist Hypothesis is more complex. It appears that the insights of a contrastivist approach can best be captured in a serial (derivational) model of OT in which some of the restrictions of the standard parallel version are relaxed. I will begin with a brief review of the essentials of OT (section 6.2), and then I will present some early treatments of contrast within OT(section 6.3). I will consider how the contrastive hierarchy might be incorporated into OT in section 6.4. Related issues are discussed in sections 6.5–6.7. Dispersion-theoretic approaches to contrast developed within OT are considered in section 8.4. Optimality Theory OT (Prince and Smolensky 2004, first published 1993) is a theory of constraint interaction that posits violable ordered constraints. For example, to account for the universal preference for syllables to have onsets and to avoid codas, Prince and Smolensky posit the constraints in (1).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".