Pro-Black, Pro-White, or Proactive: Examining Predictors of Implicit Racial Bias in Black Participants
Bibliographic record
Abstract
The majority of research examining implicit racial bias has focused on the biases held by White participants (Jost, Banaji, & Nosek, 2004). By contrast, the implicit racial bias of minority group members has been largely overlooked, despite the potential for these associations to provide new insight into the nature of implicit social cognition. In the current research, I extended previous findings by examining predictors of implicit racial bias for Black participants. Specifically, across three studies conducted in two cultural contexts, I examined whether implicit racial bias was related to Black participants racial ideologies, defined as an individuals philosophy about how racial group members should live and interact with other groups in the larger society (Sellers, et al., 1997, pp.806). Consistent with my expectations, implicit racial bias, as measured by the Implicit Association Test (IAT; Greenwald et al., 1998; 2003; Studies 1 & 2) and the Affective Misattribution Procedure (AMP; Payne et al., 2005; Study 3) was significantly correlated with racial ideologies. However, the specific relationship depended on the cultural context as well as the implicit measure. In Study 1, within the predominantly White Canadian context, Nationalist ideology was negatively correlated with implicit pro-White bias. By contrast, in Study 2, within the predominantly Black Jamaican context, Humanist ideology positively predicted pro-White bias (Study 2). In Study 3, again conducted in the predominantly White Canadian context but with a different measure of implicit racial bias (AMP), Nationalist ideology negatively predicted implicit pro-White bias, while both Assimilation and Humanist ideologies were positive predictors of implicit pro-White bias. In Study 3, explicit racial attitudes, system justification and individual versus collective success orientation were also significantly correlated with implicit racial bias as measured by the Affective Misattribution Procedure (AMP; Payne et al., 2005). As expected, however, ideologies accounted for unique variance in implicit racial bias. The implications of these findings for understanding implicit racial bias in Blacks, in predominantly White and Black contexts, are discussed.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".