I Don't See Race (or Conflict): Strategic Descriptions of Ambiguous Negative Intergroup Contexts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it