Generic language and speaker confidence guide preschoolers’ inferences about novel animate kinds.
Bibliographic record
Abstract
We investigated the influence of speaker certainty on 156 four-year-old children's sensitivity to generic and nongeneric statements. An inductive inference task was implemented, in which a speaker described a nonobvious property of a novel creature using either a generic or a nongeneric statement. The speaker appeared to be confident, neutral, or uncertain about the information being relayed. Preschoolers were subsequently asked if a second exemplar shared the same property as the first. Preschoolers consistently extended properties to additional exemplars only when properties were described in a generic form by a confident or neutral speaker. If a speaker appeared to be uncertain or if statements were made in a nongeneric form, properties were not consistently extended beyond the first exemplar. The findings demonstrate that children integrate the inductive cues provided by generic language with social cues when reasoning about abstract kinds.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; 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".