Bridging Racial Divides: Social Constructionist (vs. Essentialist) Beliefs Facilitate Trust in Intergroup Contexts
Bibliographic record
Abstract
Trust serves as the foundation for social harmony and prosperity, but it is not always easy to build. When people see other groups as different, e.g., members of a different race or ethnicity, the perceived boundary often obstructs people from extending trust. This may result in interracial conflicts. The current research argues that individual differences in the lay theory of race can systematically influence the degree to which people extend trust to a racial outgroup in conflict situations. The lay theory of race refers to the extent to which people believe race is a malleable social construct that can change over time (i.e., social constructionist beliefs) versus a fixed essence that differentiates people into meaningful social categories (i.e., essentialist beliefs). In our three studies, we found evidence that social constructionist (vs. essentialist) beliefs promoted interracial trust in intergroup contexts, and that this effect held regardless of whether the lay theory of race was measured (Studies 1 and 3) or manipulated (Study 2), and whether the conflict was presented in a team conflict scenario (Study 1), social dilemma (Study 2), or a face-to-face dyadic negotiation (Study 3). In addition, results revealed that the lay theory’s effect on interracial trust could have critical downstream consequences in conflict, namely cooperation and mutually beneficial negotiation outcomes. The findings together reveal that the lay theory of race can reliably influence interracial trust and presents a promising direction for understanding interracial relations and improving intergroup harmony in society.
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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 | 0.004 | 0.016 |
| 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 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".