An expletive negation unlike any other in Québec French
Bibliographic record
Abstract
This paper explores ‘expletive’ uses of the negative marker pas in Québec French (QF) (Kemp 1982, Larriv´ee 1996), which despite checking every diagnostic for expletive negation (ExN), do not pattern with previously documented cases of ExN. We show that most previous accounts of ExN can thus not explain ExN pas’s distribution. Building on van der Wouden’s (1994) approach to ExN as negative polarity items (NPIs), and adopting an alternative-based account of NPIs (Krifka 1995, Lahiri 1998, Chierchia 2013, a.o.), wepropose a preliminary analysis of ExN pas as part of a ‘complex’ NPI. That is, ExN pasrealizes one of two pieces in the composition of an NPI: (i) it does not contribute existential quantification of its own, but (ii) requires that the predicative existential expression it co-occurs with activate a set of domain alternatives. Though this analysis stands out in making a number of correct predictions about the distribution of ExN pas, it faces an empirical challenge, which we ultimately leave as an issue for future work.
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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 | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".