Who Has More Furniture? Context Effects on the Quantification of Mass vs. Count Superordinate Nouns
Bibliographic record
Abstract
In many languages, words in count syntax quantify over countable individuals (e.g., too many strings), while mass nouns often don’t (e.g., too much string). Theories differ in how to characterize nouns that violate this pattern, such as object-mass nouns (e.g. furniture, clothing). These nouns exhibit mass syntax, but often quantify by number (Barner & Snedeker, 2005). On one hypothesis, the individuation of object-mass nouns is lexically specified (Bale & Barner, 2009). Another argues that, while count nouns always quantify by number, object-mass nouns have different quantification criteria depending on context (Rothstein, 2010), including function fulfillment (McCawley, 1975). We evaluated these hypotheses by comparing English quantity judgments for object-mass nouns to (1) superordinate count nouns, and (2) French judgments for translations of object-mass nouns. In each case, we found that object-mass nouns behaved like count nouns, and were no more susceptible to contextual effects. These findings support the idea that object-mass nouns specify individuation lexically.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".