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