Bibliographic record
Abstract
Abstract In certain uses, adjectives appear to make the semantic contribution normally associated with adverbs. These readings are often thought to be a peripheral phenomenon, restricted to one corner of the grammar and just a handful of lexical items. I’ll argue that it’s actually considerably more general than is often recognized, and that it admits two fundamentally different modes of explanation: in terms of the syntactic machinery that undergirds these structures and in terms of the ontology of the objects manipulated by its semantics. Both modes of explanation have been suggested for some of the puzzles in this domain, and I’ll argue both are necessary. With respect to adjectives includingaverageandoccasional, the key insight is that their lexical semantics is fundamentally about kinds. But to arrive at a more general theory of adverbial readings, it is also necessary to further articulate the compositional semantics. In this spirit, I’ll argue that these adjectives actually have the semantic type of quantificational determiners likeevery. If this way of thinking about adverbial readings is on the right track, it instantiates a means by which these two distinct modes of explanation—and the distinct aspects of cognition they may ultimately be associated with—both play a crucial role in bringing about the apparently aberrant behavior of this class of adjectives.
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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 | 0.001 |
| 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 | 0.007 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".