Future time reference and viewpoint aspect: Evidence from Gitksan
Bibliographic record
Abstract
In many languages, future time reference can be conveyed in more than one grammaticized way. An example is English, which uses will and be going to. These two forms make different semantic and pragmatic contributions, and the source of the contrast is a matter of debate. For example, Copley (2009) argues that both will and be going to have a modal component, but be going to also contains progressive aspect. Klecha et al. (2008) and Klecha (2011) also posit modality for both forms, but argue that will introduces obligatory modal subordination; crucially for them, be going to does not contain the progressive. In this paper, we address the following three questions: (a) Do any other languages show a contrast between will-like and be going to-like futures? (b) Is there cross-linguistic support for the proposal that some futures contain progressive aspect? (c) Can cross-linguistic data shed light on the debate about English?Our answer to all three questions is ‘yes’. We show that (a) Gitksan (Tsimshianic) displays a contrast between will-like and be going to-like futures; (b) their distribution provides support for progressive aspect in the latter type of futures; and (c) Gitksan contributes cross-linguistic evidence to the debate about the nature of futures in English. We provide an analysis that combines elements of both Copley’s (2009) and Klecha’s (2011) accounts. More generally, we argue that different future constructions across languages are derived by combining at least the following three building blocks: prospective aspect, a modal, and the progressive.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".