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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.003 |
| 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".