One Touse, Two Tice, Three...What? How Children Learn Irregular Plurals Using Conventionality
Bibliographic record
Abstract
Most of the time, the rules that change a word's meaning are fairly constant (e.g., add –s to make a noun plural); however, they do not always apply. But how do young word learners even consider the possibility that "feet" is the plural of "foot," when the word "foots" should do equally well? This study suggests that children may learn an irregular novel plural form of a noun better from the person who created an object, than from someone who only found it. To do this, we played a naming game with 3 and 4 year old children, where we taught them regular and irregular plurals for unfamiliar objects, and evaluated whether the group of children who were told that the experimenter had made the objects learned the plurals more than those who were told that the experimenter had found them. Although hampered by a small sample, preliminary results showed that 4‐year‐olds learned irregulars slightly more often in the made condition than the found condition. The younger group did not produce irregulars as readily, and was not affected by whether the novel toy was made or found.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".