Bibliographic record
Abstract
The principal goal of the present investigation was to ascertain the role of single-word and sentential contexts in word learning at 15 and 18 months of age. Secondary goals included the investigation of gender differences in word learning and the relations between receptive vocabulary and word learning. These goals necessitated the development of a non-interactive procedure that would permit precise control over the input. Experiments 1 and 2 were concerned with these procedural issues. When 18-month-old girls and boys witnessed audio-visual presentations featuring a speaker who talked about objects while looking at them, the girls learned the words, but the boys did not (Experiment 1). When the task was simplified, 15-month-old girls succeeded when the speaker's image was out of view, but not when it was in view (Experiment 2). The simplified task with out-of-view speaker was used for the remaining experiments. When the target words were presented in single-word or sentential contexts (Experiment 3), there were age- and gender-related differences. Specifically, 15-month-old boys succeeded only in the context of single words, and 15-month-old girls succeeded only in the context of sentences. By contrast, 18-month-old girls and boys learned in both contexts, and they required fewer exposures than did younger learners. When the sentence frames were replaced by tones (Experiment 4), 15- and 18-month-old girls and boys failed to link words with objects. Contrary to expectations, toddlers' receptive or expressive vocabulary was unrelated to their performance on the word-learning task. The results are discussed in terms of contemporary accounts of word learning.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".