Training and Timing Local Scalar Enrichments under Global Pragmatic Pressures
Bibliographic record
Abstract
Elementary sentences containing the quantificational determiner some seem to be ambiguous between a ‘weak’ existential meaning ∃ and a ‘strengthened’ some but not all meaning ∃+. The strengthened meaning is commonly assumed to be the output of a general enrichment mechanism, call it G (for ‘global’), that applies to the weak meaning of the sentence: G(∃) = ∃+. The application of G has been shown to come with a processing cost (e.g. Bott & Noveck 2004). We used a self-paced reading task together with offline comprehension questions to investigate the interpretation of sentences containing some when embedded inside a disjunction, a position that G cannot access. Our findings suggest (i) that the strengthened meaning ∃+ is available in embedded positions, suggesting that a mechanism of local strengthening L must be available: L(∃) = ∃+, (ii) that local enrichment can be facilitated by global pragmatic pressures (Chierchia et al. 2008; Mayr & Romoli 2014), (iii) that subjects can be quickly trained to systematically prefer one of G or L to the other, (iv) that application of L, like the application of G, comes with a processing cost. We highlight consequences of our findings for debates about the characterization of enrichment mechanisms, focusing on the relation between G and L.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".