Bibliographic record
Abstract
This study investigates the effect of a language-wide lack of pragmatic resuppositions on focus marking (often taken to be inherently presuppositional). The language of investigation is Nɬeʔkepmxcin (Thompson River Salish). I show that discourse participants treat presuppositions triggered by focus in the same way as lexical presuppositions. Addressees do not challenge presuppositions that they do not share (strikingly unlike in English). Speakers, however, typically avoid using presuppositions not shared by the addressee. As a result, speakers avoid using their own utterances to mark narrow focus at all, a striking difference from English. I argue that this is due to another pragmatic constraint subject to cross-linguistic parameterization: while the speaker’s own utterance counts as being in the common ground for the purposes of marking presuppositions in English, Salish speakers do not generally mark presuppositions unless they have overt evidence that the addressee shares these presuppositions. This results in a radically different focus marking strategy within a discourse turn as opposed to across discourse turns.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".