I Don't See Race (or Conflict): Strategic Descriptions of Ambiguous Negative Intergroup Contexts
Bibliographic record
Abstract
Abstract Despite current societal trends to encourage diversity, individuals often avoid acknowledging race, and we suggest also conflict, because of concerns about appearing prejudiced. The present research investigated the use of racial color and conflict blind strategies in an ambiguous negative intergroup context. In three studies, we assessed whether people acknowledged race and conflict using a novel ambiguous context task. Study 1 demonstrated that when describing an intergroup interaction with a photograph of Black and White males bumping into one another, only 27% of participants used racial labels and approximately half (53%) mentioned conflict. In Study 2, when participants described two White males in the same situation, significantly fewer participants mentioned conflict compared to when the photograph depicted a Black and White male actor, but rates of mentioning race were not different. Finally, in Study 3, when participants were instructed to use race when describing the actors, they mentioned conflict significantly less than when they were free to avoid racial labels. These latter results suggest that although racial color blindness may be used to appear unbiased, when this strategy is unavailable, people may resort to not referencing intergroup negativity. Together these findings indicate that racial color and conflict blindness may work in conjunction as compensatory strategies to appearing nonprejudiced.
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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 | 0.013 |
| 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 | 0.001 | 0.001 |
| 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".