Countability in Absence of Count Syntax: Evidence from Japanese Quantity Judgments
Bibliographic record
Abstract
We investigated the interaction of mass-count syntax and item-specific wordmeanings by comparing quantity judgments in two mass-count languages(English, French) and a classifier language (Japanese). Speakers of bothEnglish and Japanese based quantity judgments on volume for substance-massterms (e.g., judging two large portions of toothpaste to be moretoothpaste thansix tiny portions) but on number for count nouns (e.g., shoes) andobject-mass nouns (e.g., judging that six small pieces of furniture are morefurniture than two large pieces). For words that can be used in either massor count syntax in English (e.g., string), English quantity judgmentsshifted as a function of mass-count syntax (i.e., based on number when usedin count syntax, but on volume when used in mass syntax), whileapproximately 50% of Japanese quantity judgments were based on number,falling between English mass and count judgments. For words that are massnouns in English but count nouns in French (e.g., spinach), quantityjudgments shifted as a function of syntax between these languages, whileJapanese judgments were not different from the count judgments of Frenchspeakers, and were based mainly on number. We argue that, across languages,mass-count syntax is not necessary for nouns to specify individuation, butacts to select from among universally available lexical meanings.
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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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".