Synchrony and diachrony of postverbal negation in Jodï-Sáliban
Bibliographic record
Abstract
Abstract This article proposes a detailed comparative treatment of negation in the Jodï-Sáliban language family (Venezuela-Colombia, Northwest Amazonia, South America), which consists of four languages: Jodï [yau], Sáliba [slc], Piaroa [pid] and Mako [wpc]. This comparative analysis of negation strategies across the four languages in the family not only offers an overview of negation strategies in this language family but also allows for conclusions to be drawn on the cognacy of the different constructions and markers as well as on the sources of the main negation strategies. Specifically, I show that, even though certain markers are not cognate, negation in these languages has – as far back as the documentation goes – always been postverbal and suggest that postverbal negation can be diachronically stable. This research thus offers an in-depth analysis of negation in Jodï-Sáliban, a language family that remains underdescribed, and, crucially, contributes to our understanding of postverbal negation and its sources.
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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 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".