Diversity perceptions and neighborhood preferences for visual representations of mixed racial groups
Bibliographic record
Abstract
Although the term "diversity" is ubiquitous in modern day society, the meaning of the term is not well understood, and it is unclear when people perceive groups to be more or less diverse. Across five experiments, we examined perceptions of racial diversity and preferences for living in diverse contexts related to visual representations of groups. While recent theorizing suggests that a greater number of low-status racial minorities may contribute to perceiving more diversity and determine living preferences, our findings indicate the importance of the race of the perceiver in these processes. Although White and Black participants rated majority Black compared to majority Asian groups as more diverse, followed by majority White targets (cf. Experiment 4), Asian participants did not differ in their diversity ratings across the different racial compositions. Notably, White and Asian participants rated majority White and majority Asian compared to majority Black neighborhoods as more desirable. Black participants, in contrast, consistently rated majority Black compared to majority White and majority Asian neighborhoods as more desirable and did not distinguish between the latter two contexts. Together these findings provide new evidence about how people define diversity and the importance of target-level factors, perceiver-level factors, and the interplay between these factors on perceptions of racial diversity and inclusivity.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".