When hearsay trumps evidence: How generic language guides preschoolers’ inferences about unfamiliar things
Bibliographic record
Abstract
Two experiments investigated 4-year-olds' use of descriptive sentences to learn non-obvious properties of unfamiliar kinds. Novel creatures were described using generic or nongeneric sentences (e.g., These are pagons. Pagons / These pagons are friendly). Children's willingness to extend the described property to a new category member was then measured. The results of Experiment 1 demonstrated that children reliably extended the property to new instances after hearing generic but not nongeneric sentences. Further, the influence of generic language was much greater than effects related to the amount of tangible evidence provided (the number of creatures bearing the critical property). Experiment 2 revealed that children continued to extend properties mentioned in generic descriptions even when incompatible evidence was presented (e.g., an example of an unfriendly ‘pagon’). The findings underscore preschoolers' keen understanding of the semantics of generic sentences and suggest that inferences based on generics are more robust than those based on observationally grounded evidence.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".