Does the conceptual distinction between singular and plural sets depend on language?
Bibliographic record
Abstract
Previous studies indicate that English-learning children acquire the distinction between singular and plural nouns between 22 and 24 months of age. Also, their use of the distinction is correlated with the capacity to distinguish nonlinguistically between singular and plural sets in a manual search paradigm (D. Barner, D. Thalwitz, J. Wood, S. Yang, & S. Carey, 2007). The authors used 3 experiments to explore the causal relation between these 2 capacities. Relative to English, Japanese and Mandarin had impoverished singular-plural marking. Using the manual search task, in Experiment 1 the authors found that by around 22 months of age, Japanese children also distinguished between singular and plural sets. Experiments 2 and 3 extended this finding to Mandarin-learning toddlers. Mandarin learners who were 20-24 months of age did not yet comprehend Mandarin singular-plural marking (i.e., yige vs. yixie, or -men), yet they did distinguish between singular and plural sets in manual search. These experiments suggest that knowledge of singular-plural morphology is not necessary for deploying the nonlinguistic distinction between singular and plural sets.
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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 | 0.002 | 0.009 |
| 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 | 0.006 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".