Formalizing the connection between opaque and exceptionful generalizations
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 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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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