Formalizing the connection between opaque and exceptionful generalizations
Bibliographic record
Abstract
This paper proposes an account of an opaque generalization (Canadian Raising; Chambers 1973) in terms of indexed constraints in OT (Pater 2000, 2010). This approach formalizes the idea, championed in previous work on Canadian Raising (Mielke et al. 2003; Pater 2014) and opacity more broadly (Lubowicz 2003; Sanders 2003, 2006), that opaque generalizations have a stronger connection to the lexicon and/or exceptionality than to the grammar proper. These previous approaches tend to yield non-restrictive accounts of opaque generalizations (ones that do not easily extend the pattern to novel items), which I show also holds for an account of opaque Canadian Raising in terms of constraints indexed to whole morphemes (Pater 2000, 2010). To counter this, I propose so-called extended indexation: a blend of segmentally local indexation (Temkin-Martínez 2010; Rubach 2013, 2016; Round 2017) and binary indexation (Becker 2009) that goes back to the account for exceptions from Chomsky and Halle (1968). I show that this kind of indexation offers a restrictive account of opaque Canadian Raising, compatible with the fact that Raising is productive (Idsardi 2006).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".