Children's sensitivity to authenticity in their extension of brand names
Bibliographic record
Abstract
Learning about the countless manufactured resources that surround us involves learning that they may be categorized according to the identity of their maker (i.e., their brand). Prior work indicates that children know some brand names as young as two years but has not examined whether young children understand that these expressions should be extended only to authentic products (i.e., those with a historical link to a particular maker) regardless of their perceptual appearance (i.e., the presence of a familiar trademark). Thirty-two 4-year-olds, 32 6-year-olds and 32 adults participated. Adults and 6-year-olds, but not 4-year-olds, systematically extended familiar brand names from a target product to other products that shared the same maker, even when this extension could not be based on perceptual appearance. By six years, children have begun to understand that a non-obvious historical property – maker identity – underlies the categorization of manufactured objects by brand.
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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 | 0.002 | 0.013 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".