The development of Tibetan children’s racial bias in empathy: The mediating role of ethnic identity and wrongfulness of ethnic intergroup bias.
Bibliographic record
Abstract
on Oct 07 2024 (see record 2025-32719-001). In the article, the authors wish to remove biased language and inappropriate discussion surrounding the comparison between the Tibetan sample and the non-Tibetan samples, and the text discussing the development of Tibetan children's awareness of their own racial prejudice. The necessary corrections are present in the erratum.] Objectives: Individuals often automatically have more empathy for same-race members. However, there are no studies on racial bias in empathy (RBE) among Tibetan school-aged children. The present study aimed to examine the development of RBEs, including racial bias in cognitive empathy, affective empathy, and behavioral empathy, in Tibetan school-aged children. METHOD: = 60, aged 7-12), Tibetan children's ethnic identity and the awareness of the wrongfulness of ethnic intergroup bias were added to examine the underlying mechanism. RESULT: Results found that RBEs increased among Tibetan children aged 7-10 and decreased among those aged 11-12, Moreover, we analyzed age as a continuous variable and found that 10 years old was the inflection point in the development of RBEs in Tibetan children. Importantly, children aged 11-12 years old realized more wrongfulness of ethnic intergroup bias than children aged 7-10. The ethnic identity of Tibetan children aged 7-10 mediated the relation between age group and RBEs. And the wrongfulness of ethnic intergroup bias mediated the link between age group and RBEs in Tibetan children aged 9-12. CONCLUSION: Our study sheds light on the development of RBEs in Tibetan school-aged children and highlights the importance of identifying the appropriate timing for intervening in prejudice. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".