Bibliographic record
Abstract
Abstract This paper provides an analysis of the ‘frustrative’ marker séna7 in St’át’imcets (Lillooet Salish), and compares it to similar elements cross-linguistically. Séna7 appears in a range of discourse contexts, including when events have an unexpected outcome, fail to continue, or fail to take place optimally. We argue that séna7 felicitously applies to a proposition p only if there is a salient true proposition q and the speaker did not expect p and q to both be true. Séna7 encodes epistemic modality, refers only to the speaker’s epistemic state (ignoring the common ground), and has no effect on at-issue truth conditions (séna7(p) entails p). We show that séna7 provides a diagnostic for distinguishing between entailments and implicatures in the language, and a clear diagnostic for the distinction between futures and prospective aspects. We compare séna7 with similar elements in Tohono O’odham, Kimaragang and Tagalog. We argue that séna7 and the Kimaragang frustrative can be captured by the same analysis once independent features of their tense/aspect systems are taken into account. Following Kroeger (2017. Frustration, culmination and inertia in Kimaragang grammar. Glossa: A Journal of General Linguistics 2(1). 56. 1–29), but pace Copley and Harley (2014. Eliminating causative entailments with the force-theoretic framework: The case of the Tohono O’odham frustrative cem. In Bridget Copley & Fabienne Martin (eds.), Causation in grammatical structures (Oxford Studies in Theoretical Linguistics 52), 120–151. Oxford: Oxford University Press), we argue that frustratives should not be unified with non-culminating accomplishments, and can be analyzed without appealing to causality or efficacy.
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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 | 0.003 |
| 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 | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".