Input That Contradicts Young Children’s Strategy for Mapping Novel Words Affects Their Phonological and Semantic Interpretation of Other Novel Words
Bibliographic record
Abstract
Children tend to choose an entity they cannot already label, rather than one they can, as the likely referent of a novel noun. The effect of input that contradicts this strategy on the interpretation of other novel nouns was investigated. In pre- and posttests, 4-year-olds were asked to judge whether novel nouns referred to "name-similar" familiar objects or novel objects (e.g., whether japple referred to an apple or a binder clip). During an intervening treatment phase, they were asked to pick the referents of novel nouns from pairs of familiar objects (Experiments 1 and 3) or were taught subordinate names for familiar objects (Experiment 2). Most resisted the lure of phonological similarity in the pretest but increased selection of name-similar familiar objects over novel ones in the posttest. In Experiment 3, which involved monosyllables that differed in initial phoneme from the familiar words, treatment produced this effect only when accompanied by a rhyme-sensitization procedure. Experiment 2 included two other age groups: 2-year-olds, who were less resistant to phonological similarity in the pretest and responded to the treatment like the 4-year-olds; and adults, who nearly always selected the novel objects in the pretest and posttest. For children, the impact of treatment was positively associated with ability to detect phonological similarity and negatively associated with vocabulary size.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".