Bibliographic record
Abstract
Abstract This article provides an analysis of verum marking in the Tsimshianic language Gitksan. Original fieldwork data are provided to show that Gitksan verum is very similar in its distribution and discourse effects to English verum, but displays two interesting differences. First, Gitksan verum is not marked by focal stress, but by a dedicated particle (k'ap). Second, Gitksan verum does not require givenness of the core propositional material. I argue that when applied to a propositionp,k'apis (a) disallowed discourse-initially or in answer to awh-question; (b) felicitous when responding to a prior assertion or implication of ¬p;and (c) felicitous in other contexts only if there is prior controversy in the discourse about the truth ofp.I show that the semantic contribution of the Gitksan verum particle can be captured by a discourse management analysis:verum(p) is licensed only when the speaker believes that some interlocutor is committed to ¬p.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".