Bibliographic record
Abstract
This paper addresses the ambiguity of comparatives that contain a permission-related existential modal in their than-clause. For example, given the context that the interval of permitted speed is between 35 and 50 mph, the sentence Lucinda is driving less fast than allowed is ambiguous between two readings: (i) her speed is below the minimum (i.e., 35 mph); (ii) her speed is below the maximum (i.e., 50 mph). Previously, this ambiguity has been attributed to either the scopal interaction between a negation element and a modal (Heim 2006a) or the optional application of a silent operator (Crnic 2017). Here we show that these two lines of accounts under- or over-generate. Instead, we propose that the source of this ambiguity is located in the ambiguous answerhood for wh-questions corresponding to this kind of than-clauses (e.g., how fast is Lucinda allowed to drive). The current proposal consists of three parts. First, based on Zhang & Ling (2015, 2017a,b), we adopt a generalized interval-arithmetic-based recipe for computing the semantics of comparatives. Second, the semantics of than-clauses is considered equal to that of short answers to corresponding wh-questions. Third, since the use of existential priority modals in wh-questions leads to the ‘mention-some/mention-all’ ambiguity for answerhood, we propose that this ambiguity projects in further derivation and leads to the two readings for comparatives like the Lucinda sentence.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".