Young Children’s Trust in the False Testimony of ingroup versus outgroup speakers
Bibliographic record
Abstract
The present research explores whether young children display different levels of trust in the testimony of speakers from their own social group (ingroups) versus another social group (outgroups). Three- and 4-year-old children watched through a window as an adult hid a toy in one of three containers. The adult then told the child that she had put the toy in a container different from the one where it was actually hidden (i.e., false testimony). At the end the child was asked to retrieve the toy. The adult was either a Caucasian, native English speaker ingroup) or an Asian English speaker with a noticeable foreign accent (outgroup). Four-year-old children were credulous to the false testimony of the ingroup speaker, despite their firsthand observations, but were skeptical and relied on their own observations when the false testimony was provided by the outgroup speaker. In contrast, 3-year-old children remained credulous to the false testimony of both speakers. These findings were discussed in relation to children’s early preferences for ingroup members and the developmental shift in skepticism displayed by 4-year-old, but not 3-year-old children. This research will make a unique contribution to our understanding of how young children selectively learn from other people and why they remain credulous to some speakers, but not to others.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".