The Role of Culture in the Development of Prejudice and Moral Reasoning
Bibliographic record
Abstract
Prejudices develop early in childhood and can drive disparities in how children treat members of different social groups. These biases can progress into xenophobic and discriminatory acts in adulthood, making it crucial to address them early in life. To effectively do so, we must first identify to what extent prejudices are inevitable or driven by cultural factors. This dissertation investigates the role of culture in children’s development of group biases and other moral processes.In Chapter 1, I find that assigning children to artificially constructed minimal groups (e.g., an Orange or Green group) is sufficient to induce an ingroup bias in children’s sharing behavior, and this bias overrides the desire to appear fair and generous to others. These findings suggest that children are predisposed to favor their ingroup over outgroups, but the implications for real-world groups are unclear. In Chapter 2, I address the limitations of Chapter 1 by examining how Iranian children perceive real-world outgroups that differ from their own in similarity, sociopolitical relations, and status. I find that children do not view all outgroups interchangeably, but rather base their group preferences on the relative status of the group in question. These findings highlight the need for more research in non-Western societies to further understand the complexities of children’s intergroup attitudes. Finally, in Chapter 3, I highlight the issues that arise when researchers use a standard set of measures developed primarily for Western groups to conduct cross-cultural comparisons. I propose a new two-stage model that combines standardized methods with culturally tailored items to achieve greater validity of measures and reliability of findings across different cultural groups. I demonstrate that this two-stage model is effective in capturing the moral/conventional distinction in children from Canada, India, Iran, and Korea.This dissertation provides key insights into the cognitive and cultural mechanisms that shape childhood prejudice and highlights new approaches for assessing the role of culture in children’s moral reasoning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".