Polar questions in nɬeʔkepmxcín: monopolar, bipolar, and exhaustive
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it