Bibliographic record
Abstract
It has been proposed that the part structures of denotations of plurals ‘project’ to the denotations of expressions including those plurals (e.g., Gawron & Kehler 2004, Kubota & Levine 2016, Schmitt 2019/2020). If such a plural projection is possible, not only plural DPs but also expressions including those plural DPs denote pluralities (e.g., saw the two recipes denotes a plurality {SAW(recipe1),SAW(recipe2)} instead of a singularity {SAW({recipe1,recipe2})}). One piece of support for plural projection comes from Schmitt’s (2020) observation about ‘non-local’ cumulativity. In this paper, I further examine when cumulativity is available non-locally, and show that a source of cumulativity in the literature (e.g., Krifka 1989, Kratzer 2007, Harada 2022b) can capture all the relevant non-local cumulativity data without plural projection while an analysis with plural projection can capture only a proper subset of those data. Therefore, this paper concludes that the relevant non-local cumulativity does not support the need of plural projection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.004 | 0.013 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 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".