Non-canonical Very as a Degree Modifier of NPs in English
Bibliographic record
Abstract
The aim of this paper is to investigate the meaning of constructions with a non-canonical use of very inside NPs and to propose a unified formal semantic analysis for the degree modifier very. We adopt the notion of scalar properties and take as a starting point the fact that very is a typical degree modifier that boosts the degree of open-scale adjectives (e.g. tall) (cf. Kennedy & McNally, 2005). In this work, we focus on what we name non-canonical very: the modification of very on NPs (e.g. the very house John lived in). Our methodology consists of three major steps: firstly, we selected sentences with non-canonical very from The British National Corpus. Then, we selected sentences from five American and British novels published in the 19th and 20th centuries, comparing the sentences with their translations into Portuguese. Based on a first analysis of these sentences and on Matthewson’s (2004) methodology, we proceed to controlled elicitation of contexts with the participation of a native-English speaker of Wales. Data collected present definite DPs and complex NPs, what supports a proposal that consider modification of a scale that is closed and contextually dependent. We argue in favor of an analysis that gives a uniform lexical entry to very and assume that the difference on interpretation of canonical and non-canonical modification is due to scalar properties of the modified predicate. Canonical very modifies lexical open scales whereas non-canonical very modifies contextual closed scales of precision and produces an exhaustive interpretation. The study reveals the importance of logical scalar properties for the semantic investigation of degree modifiers.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".