Bibliographic record
Abstract
Children typically explore, that is, select information sources that are unfamiliar, during information-seeking; however certain variables can limit this exploratory behaviour. The study investigated children's likelihood to explore based on their representations of knowledge distribution. Knowledge distribution is the idea that information is spread differently across populations. Some information is commonly known (such as traffic laws), and therefore widely-distributed, whereas other subjects are less commonly known (such as laws of physics) and thus narrowly-distributed. People who know about narrowly-distributed areas of knowledge are classified as experts in that subject. In the experiment, 4-year-olds met an informant who correctly labeled objects from fields of knowledge that were either broadly-distributed or narrowly-distributed. Next, participants viewed another object from the same field of knowledge and could choose to seek help with labeling this object from either the familiar informant or a novel informant. Children's perceptions of the distribution of knowledge influenced their choice of an informant. These data suggest that children's representations of knowledge may restrict their exploration, that is, when they are dealing with what they perceive as a narrowly-distributed topic, they will presume the familiar informant is an expert on the subject and choose that informant to provide more information.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".