Bibliographic record
Abstract
Underlying consonant sequences in Cayuga (and ill other Northernlroquoian languages) are apparently subject to phonotactic constraints. The non-randomness of underlying consonant sequences is problematic for Optimality-Theory (OT), which assumes that inputs are unconstrained (Prince & Smolensky 1993; Smolensky 1995). However, I show that apparent pllOnotactic constraints are the product of the interaction of output-based constraints: I claim that the output optimally conforms to a group of ranked constraints on syllable structure which conspire to produce a ev(v)e syllable template. The ev(v)C template predicts a maximum of two consonants word-medially; problematically, larger word-medial sequences exist. Nevertheless, the alternative of positing a larger template (such as eev(v)C) is undesirable: doing so predicts too few sites of epenthesis. Consequently, I adopt the smaller ev(v)C template and propose two explanations for the larger (3+) word-medial sequences: first, some larger sequences are subject to MERGE; that is, continuant segments in such sequences are phonetically realized as secondary articulations rather than as full segments. As a consequence, word-medial consonant sequences contain at most two stop segments (plus some continuant segments which are realized as secondary articulations). Second, exceptionally large consonant sequences containing three stops can be licenced in the Cayuga verb because the verb is a prosodic phrase (i/J) poten tially containing several prosodic words (w). Each prosodic word within the verb can have an appendix in which an extra (third) stop consonant can be licensed. In sumnzary, Cayuga has a ev(v)C template which licenses a maximum of 2 consonants word-medially; nevertheless, because of underparsing (MERGE) and verb-internal appendices, larger sequences can be realized within the verb.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".