The Effects of System Justifying Beliefs on Skin-Tone Surveillance, Skin-Color Dissatisfaction, and Skin-Bleaching Behavior
Bibliographic record
Abstract
In two studies with women living in India (Study 1, n = 177) and African American women in the United States (Study 2, n = 120), we investigated whether skin-tone surveillance, which theoretically is a manifestation of self-objectification, predicted greater skin-color dissatisfaction and greater skin-bleaching behavior. Given the existence of colorism in Indian and American societies, we expected that ideologies that rationalize and perpetuate the status quo would moderate the proposed relations. Results were consistent with objectification theory and system justification theory. The positive relation between skin-tone surveillance and skin-color dissatisfaction was weaker among women of color who more strongly (vs. weakly) endorsed system justifying ideologies, and the positive relation between skin-tone surveillance and skin-bleaching behavior was stronger among women of color who more strongly (vs. weakly) endorsed system justifying ideologies. Our results suggest that self-objectification theorists and researchers should consider culturally specific manifestations of self-objectification as well as protective and legitimating effects of system justifying ideologies. We encourage clinicians and policy makers to use public campaigns and individual-level interventions to target the norms and motivations underlying skin-bleaching. Online slides for instructors who want to use this article for teaching are available on PWQ's website at http://journals.sagepub.com/page/pwq/suppl/index
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".