Black racial phenotypicality: Implications for the #BlackLivesMatter Movement
Bibliographic record
Abstract
Black individuals with phenotypically African features tend to experience heightened discrimination and mistreatment. The current research examined how racial phenotypicality and prototypicality effect hate crime reporting metrics and beliefs about who evaluators are represented #BlackLivesMatter. Across five studies ( N = 876), results indicate that, compared to low racially phenotypic Black targets, high phenotypic targets were seen as more represented by #BlackLivesMatter (Study 1). When depicted as being the victim of a hate crime, high phenotypic targets were deemed more credible and that it was more appropriate for them to report their victimization on the #BlackLivesMatter website compared to their low phenotypic counterparts by White (Study 2a and 2c) and Black participants (Study 2b and 2c). Black (Study 2b and 2c) and White (Study 3) participants showed differences in perceptions of harm following hate crime victimization. Study 3 extended these findings to a separate manipulation of prototypicality and used a more ecologically valid context. These findings provide support for the problematic exclusivity of narrow prototypes by demonstrating their effect on beliefs about who social justice movements represent, and how they influence beliefs about victim reporting metrics.
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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 | 0.005 | 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 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".