Zero, Null Individuals, and Nominal Semantics in Cantonese
Bibliographic record
Abstract
It has been convincingly argued that English zero provides evidence for introducing null individuals into the ontology of natural language (Bylinina & Nouwen 2018). We examine ‘zero’ in Cantonese, where it provides evidence that such null individuals are a matter of crosslinguistic variation. Cantonese zero has a more restricted distribution. It occurs widely in a number of contexts, but it is systematically ruled out with ordinary classifiers. These facts, coupled with assumptions about the nature of measurement and nominal semantics, demonstrate despite its extensive use in the language, zero is impossible in precisely the uses that require null individuals. Cantonese seems to be telling us that such null individuals are simply absent from its ontology, implying an interesting difference in natural language metaphysics between the languages—and perhaps a different perspective on what theoretical shape crosslinguistic variation can take.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".