The Role of Microvariation in the Study of Semantic Universals: Adverbial Quantifiers in
Bibliographic record
Abstract
This paper addresses the question of semantic universals with a particular focus on the limits of cross-linguistic variation in the semantics of lexical expressions. I argue that the variation observed in the semantics of adverbial quantifiers in the quantification at a distance (QAD) construction (e.g. J’ai beaucoup lu de livres) between Standard European French and Québec French constitutes an important argument for the existence of polyadicity as a lexical property in natural language. Specifically, I propose that QAD sentences in the European dialect involve an unreducible binary quantifier over Æevent, objectæ pairs and that the same construction in the Canadian dialect involves a unary quantifier over individuals. I argue that the construction has the same syntax in both dialects, and therefore, the variation in the type of quantification should be attributed to variation in the lexical semantics of the adverbial quantifiers of the language. 1
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".