Bibliographic record
Abstract
The present research examined the roles of informant ethnicity and early ethnic identity development in guiding children’s selective trust across scenarios, comparing White and Chinese Canadian children with children adopted from China by White parents. Experiments 1 and 2 investigated children’s selective learning from two contrasting sources that differed in race (White versus Chinese) and spoken accent (native versus foreign accent). Experiment 1 (White experimenter) indicated that the White children preferred to learn from White informants when race was the only cue to ethnic group status; no preference was observed when race was pitted against accent. The Chinese and adopted children showed no learning preference. Children’s social preference for same-race peers was associated with a preference to learn from same-race informants. Experiment 2 (Chinese experimenter) had similar findings, except that there was no relationship between children’s racial preference and selective learning. Experiment 3 explored children’s selective credulity toward misinformation from a single source. The Chinese children were credulous toward both Chinese and White (native- or foreign-accented) informants but the White and adopted children were not credulous or skeptical, regardless of the informant’s race and accent. The present findings contribute to our understanding of how ethnic intergroup attitudes develop in children of different ethnic and social-status backgrounds, including minority-status children that reside with majority-status parents, and provide practical implications for real-world issues related to children’s eyewitness testimony in forensic contexts.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".