Modal vs. deictic evidentials in ʔayʔaǰuθəm (Comox-Sliammon)
Bibliographic record
Abstract
In this paper, we present novel data from ʔayʔaǰuθəm (a.k.a. Comox-Sliammon; an understudied Salish language) that challenge both the claim that all evidentials are epistemic modals (Matthewson 2012) and the claim that evidentials and modals are distinct, non-overlapping categories (e.g. Aikhenvald 2004, Speas 2010}. We take the defining difference between modal and nonmodal evidentials to be that modal evidentials contribute an at-issue claim involving quantification over possible worlds/situations, whereas nonmodal evidentials do not; both types of evidentials contribute information about the speaker's source of evidence for the proposition. We argue that ʔayʔaǰuθəm has two types of evidentials: one set are epistemic modals, while the other set are nonmodal deictic particles. Though we argue against the claims that evidentials are uniformly modal or nonmodal, we propose that both types of evidentials encode relations between situations (following Speas 2010).
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How this classification was reachedexpand
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".