Bibliographic record
Abstract
Racial biases exist in our society. These biases, when left unchecked, exert far-reaching adverse impacts at personal and societal levels in education, health care, employment, and justice. The preschool period is formative in the development of racial bias. Until the present thesis, it was unknown whether children at preschool-age would display racial biases against other-race people, and if yes, how to reduce them. In Chapter 2, I developed a child-friendly implicit racial bias test and found the early emergence of implicit and explicit racial biases among children as young as 3-years old. The same pattern was consistently found in three distinct cultures: China, Cameroon, and Canada. I also found that the level of racial bias was affected by perceived social status between own- vs. other-race and their level of contact with other-race members. It was the first set of studies that examine the phenomenon of racial bias in preschool-age children from various cultures. In Chapter 3, I developed a novel training method, referred to as individuation training, in which children practiced the process of treating other-race members as unique individuals rather than members of social groups. I provided the evidence that individuation training reduced implicit racial biases among children in China (Experiment 1) and Canada (Experiment 2), and that the training effects were sensitive to children’s exposure to racial diversity. In Chapter 4, I further examined the long-term effects of the novel individuation training method. I found that repeated individuation training reduced Chinese children’s implicit racial bias for the long term (60 days). Together, my thesis suggests that racial bias emerges early in preschool-age and training children to treat other-race members as unique individuals reduce implicit racial bias in both short and long terms.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".