20‐month‐olds selectively generalize newly learned word meanings based on cues to linguistic community membership
Bibliographic record
Abstract
A growing body of work suggests that speaker-race influences how infants and toddlers interpret the meanings of words. In two experiments, we explored the role of speaker-race on whether newly learned word-object pairs are generalized to new speakers. Seventy-two 20-month-olds were taught two word-object pairs from a familiar race speaker, and two different word-object pairs from an unfamiliar race speaker (four new pairs total). Using an intermodal preferential looking procedure, their interpretation of these new word-object pairs was tested using an unpictured novel speaker. We found that toddlers did not generalize word meanings taught by an unfamiliar race speaker to a new speaker (Experiment 1), unless given evidence that the unfamiliar race speaker was a member of the child's linguistic community through affiliative behaviour and linguistic competence (Experiment 2). In both experiments, generalization was observed for the word-object pairs taught by the familiar race speaker. These experiments indicate that children attend to speakers' non-linguistic properties, and this, in turn, can influence the perceived relevance of speakers' labels.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".