Events and the Ontology of Individuals: Verbs as a Source of Individuating Mass and Count Nouns
Bibliographic record
Abstract
What does mass–count syntax contribute to the interpretation of noun phrases(NPs), and how much of NP meaning is contributed by lexical items alone?Many have argued that count syntax specifies reference to countableindividuals (e.g., cats) while mass syntax specifies reference tounindividuated entities (e.g., water). We evaluated this claim using thequantity judgment method, and tested the interpretation of words used inmass and count syntax that described either protracted, “durative” events(e.g., mass: some dancing; count: a dance), or instantaneous, “punctual”events (e.g., mass: some jumping; count: a jump). For durative words,participants judged, for example, that six brief dances are more dances butless dancing than two long dances, thus showing a significant difference intheir interpretation of the count and mass usages. However, for punctualwords, participants judged, for example, that six small jumps are both morejumps and more jumping than two long jumps, resulting in no difference dueto mass–count syntax. Further, when asked which dimensions are importantfor comparing quantities of durative and punctual events, participantsranked number as first in importance for durative and punctual wordspresented in count syntax, but also for punctual words presented in masssyntax. These results indicate that names for punctual events individuatewhen used in either mass or count syntax, and thus provide evidence againstthe idea that mass syntax forces an unindividuated construal. They alsoindicate that event punctuality as encoded by verbs is importantly linkedto the individuation of NPs, and may access a common underlying ontology ofindividuals.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".