processing cost of Downward Entailingness: the representation and verification of comparative constructions
Bibliographic record
Abstract
We bring experimental considerations to bear on the structure of comparatives and on ourunderstanding of how quantifiers are processed. At issue are mismatches between thestandard view of quantifier processing cost and results from speeded verification experimentswith comparative quantifiers. We build our case in several steps: 1. We show that thestandard view, which attributes processing cost to the verification process, accounts for someaspects of the data, but fails to cover the main effect of monotonicity on measured behavior.We derive a prediction of this view for comparatives, and show that it is not borne out. 2. Weconsider potential reasons – experimental and theoretical – for this theory-data mismatch. 3.We describe a new processing experiment with comparative quantifiers, designed to addressthe experimental concerns. Its results still point to the inadequacy of the standard view. 4. Wereview the semantics of comparative constructions and their potential processingimplications. 5. We revise the definition of quantifier processing cost and tie it to the numberof Downward Entailing (DE) operators at Logical Form (LF). We show how this definitionsuccessfully reconciles the theory-data mismatch. 6. The emerging picture calls for adistinction between the complexity of verified representations and the complexity of theverification process itself.Keywords: quantification, monotonicity, negation, comparative constructions, Logical Form,adjectival antonyms, decomposition, quantifier processing, speeded verification, reactiontime.
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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 | 0.014 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.020 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".