Bibliographic record
Abstract
This paper investigates the question of whether ‘direct’ evidentials are amenable to an analysis as epistemic modals. Much recent literature advances modal analyses of evidentials, but direct evidentials pose prima facie problems for a modal analysis. In particular, typical epistemic modals differ from direct evidentials in that the former disallow direct witness, and convey reduced speaker certainty. In this paper I examine evidential elements in St’át’imcets (a.k.a. Lillooet; Salish), Gitksan (Tsimshianic), Nuu-chah-nulth (Wakashan), Cuzco and Wanka Quechua, English, Nivacle (Matacoan-Mataguayan), Cheyenne (Algonquian), Korean, and Tibetan. Based on the data presented, I propose that evidential contributions are more complex than is often assumed. Specifically, there are three different dimensions of meaning which evidentials may encode: (1) Evidence type (whether the evidence is visual, sensory, reported, etc.), (2) Evidence location (whether the speaker witnessed the event itself or merely some of its results), and (3) Evidence strength (the trustworthiness/reliability of the evidence). Each of the three dimensions has direct and indirect values, and particular evidential morphemes may be semantically complex, encoding information about one, two or all three of the dimensions. I then argue that contrary to what we might expect, evidentials which encode direct values on any of the three dimensions are compatible with modal semantics.
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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 | 0.007 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.006 | 0.018 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".