Bibliographic record
Abstract
There is debate about whether polar questions (PQs) have bipolar semantics (e.g., denoting a set of propositions { p , ¬ p }), monopolar semantics (a singleton set { p }), or both. The issue is difficult to settle using English data alone. In this paper I bring new data to bear on the debate from nɬe ʔkepmxcín (Salish). I argue that natural language has both bipolar and monopolar questions, and that nɬe ʔkepmxcín morphosyntactically distinguishes the two. I further argue that bipolar questions come in two types, which are also morphosyntactically distinguished in nɬe ʔkepmxcín: exhaustive (presupposing that p and ¬ p are the only two answer options), and non-exhaustive (allowing answers beyond p and ¬ p ). I thus argue that nɬe ʔkepmxcín’s three-way morphosyntactic contrast in polar question forms reflects a three-way semantic contrast. The nɬe ʔkepmxcín data have implications for the analysis of other languages. I argue against existing analyses of English plain PQs as either uniformly bipolar or monopolar, and in favour of an ambiguity analysis. The nɬe ʔkepmxcín data further support a distinction in at least some languages between so-called inquisitive and assertive declarative questions (DQs), rather than a unified analysis of these. Finally, nɬe ʔkepmxcín provides evidence that declarative-question-like or monopolar questions cross-linguistically need not be non-canonical, and their properties should therefore not be derived via markedness.
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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 | 0.003 | 0.007 |
| 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 | 0.013 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".