THE FACULTY OF GRADUATE STUDIES
Bibliographic record
Abstract
This dissertation addresses the linearization of affixes, and argues for a particular model of the way in which syntax maps to phonology. According to the proposal, syntax is spelled-out to phonology in minimal cycles equivalent to a single application of syntactic Merge (cf. Epstein et al. 1999). I term this proposal the local spell-out hypothesis. The empirical grounds on which this hypothesis is assessed is Nuu-chah-nulth (Nootka), a Southern Wakashan language spoken in British Columbia, Canada. Nuu-chah-nulth has a class of morphologically bound predicates termed affixal predicates which participate in a linearization strategy of suffixation. I claim that affixes in Nuu-chah-nulth are linearized at spell-out with respect to ‘hosts ’ as a consequence of the PF requirement that utterances be sequentially ordered. Spell-out induces in Nuu-chah-nulth a relationship which I label PF Incorporation. The affixal predicate ‘incorporates ’ its host in order to achieve a pronounceable form, that of a linearized affix. An affixal predicate in Nuu-chah-nulth consistently suffixes to a host chosen from its derivational sister, its complement. This suffixation pattern is subject to a string adjacency effect: an affixal predicate incorporates only the leftmost element from its complement, which
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.636 | 0.444 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".