Future interpretation in Gitksan and reduced clausal complements
Bibliographic record
Abstract
This paper explores temporal interpretations in clausal complements in Gitksan, a language without temporal morphology. Bare predicates in Gitksan can receive present or past reading. Jóhannsdóttir & Matthewson (2007) capture these readings with a covert non-future tense. For future reading, bare predicates must combine with a marker dim; in syntax, dim combines with the non-future tense. In this paper, I focus on the connection between the syntactic make-up of Gitksan complements and the availability of future-oriented reading. Assuming the non-future tense in Gitksan, I show that the attested readings can only be captured if some of the complements project TPs, while the others do not. I propose that the observed patterns follow straightforwardly from Wurmbrand’s (2001 et seq.) idea that clausal complements are of different sizes: some complements are CPs, but some can project as little as vPs. Gitksan provides support for this approach through the syntax-semantics interaction in the embedded temporal-modal domain.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".