Syntactic Cues to Individuation in Mandarin Chinese
Bibliographic record
Abstract
When presented with an entity (e.g., a wooden honey-dipper) labeled with anovel noun, how does a listener know that the noun refers to an instance ofan object kind (honey- dipper) rather than to a substance kind (wood)?While English speakers draw upon count-mass syntax for clues to the noun’smeaning, linguists have proposed that classifier languages, which lackcount-mass syntax, provide other syntactic cues. Three experiments testedMandarin- speakers’ sensitivity to the diminutive suffix -zi and thegeneral classifier ge when interpreting novel nouns. Experiment 1 foundthat -zi occurs more frequently with nouns that denote object kinds.Experiment 2 demonstrated Mandarin-speaking adults’ sensitivity to ge and -ziwhen inferring novel word meanings. Experiment 3 tested Mandarin three- tosix-year-olds’ sensitivity to ge. We discuss differences in thedevelopmental course of these cues relative to cues in English, and theimpact of this difference to children’s understanding of individuation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".