Appearances aren't everything: Shape classifiers and referential processing in Cantonese.
Bibliographic record
Abstract
Cantonese shape classifiers encode perceptual information that is characteristic of their associated nouns, although certain nouns are exceptional. For example, the classifier tiu occurs primarily with nouns for long-narrow-flexible objects (e.g., scarves, snakes, and ropes) and also occurs with the noun for a (short, rigid) key. In 3 experiments, we explored how the semantic information encoded in shape classifiers influences language comprehension. When judging the fit between classifiers and depicted objects in an explicit ranking task, Cantonese speakers evaluated classifier-noun pairings solely in terms of grammatical well-formedness and showed no separate sensitivity to the shape features of objects. In an eye-tracking task (Experiment 2), we also found little sensitivity to shape classifier semantics during real-time comprehension. However, in a subsequent experiment in which referent objects lacked the prototypical features for their accompanying classifiers (Experiment 3), an influence of shape semantics was found in participants' incidental fixations to nontarget objects. We conclude that shape classifiers influence referential interpretation primarily through their grammatical constraints, consistent with the agreementlike nature of classifiers in general. The role of shape classifiers' semantics on processing is apparent only in specific circumstances.
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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 | 0.003 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".