Playing to their strengths: Can focusing on typical in‐group strengths be detrimental to people of colour?
Bibliographic record
Abstract
Abstract With increasing awareness about racism, portrayals of communities of colour are shifting away from negative representations. Emphasizing their strengths could counter negative stereotypes about who they are and low expectations for who they can be, but could also backfire. In two experiments centring adolescents (n = 198) and adults of colour (n = 321), the effect of reflecting on a typical strength was moderated by perceived misalignments between racial/ethnic and ideal future selves (i.e., ethnic–ideal self‐discrepancy). For participants perceiving them as aligned, reflecting on a typical in‐group strength reduced actual–ideal self‐discrepancy. However, for participants perceiving them as misaligned, reflecting on a typical in‐group strength increased actual–ideal self‐discrepancy. Reflecting on a typical strength also indirectly influenced engagement, through actual–ideal self‐discrepancy. Reflecting on an atypical in‐group strength did not yield significant effects. Thus, emphasizing typical aspects of stigmatized communities, even when positive, sometimes impede identity and motivation.
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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 | 0.003 | 0.010 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".