The Kid’s Kid(’s) Bed: Generic or Possessive? A Mandarin Insight
Bibliographic record
Abstract
The recursive computational mechanism generates an infinite range of expressions. However, little is known about how different concepts interact with each other within recursive structures. The current study investigated how Mandarin-speaking children dealt with possessives and generics in recursive structures. The picture-matching task showed that Mandarin-speaking children 4 to 6 had a bias for generics in ambiguous possessive constructions in Mandarin, where the genitive maker was covert (e.g., Yuehan de baobao chuang John’s kid bed, where baobao chuang kid bed has both a generic interpretation and a referential interpretation). It was found that that Mandarin-speaking children below 6 had a non-recursive interpretation of the possessive John’s kid(’s) bed, and instead understand kid’s bed to refer generically to a type of bed. This finding suggests that semantics does not parallel syntax in the acquisition of indirect recursion, in line with the prediction of the generic-as-default hypothesis which claims that generics are the default mode of representation of ambiguous statements when the statement can be either generic or non-generic. The delayed recursive possessive interpretation suggests that the full determiner phrase is acquired later than a noun phrase modification, which is universal in all languages. We also discuss the role of the overt functional category in the acquisition of indirect recursion.
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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 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| 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".