Internalized Racism, Family Social Support, Ethnic-Racial Centrality, and Self-Esteem Among BIPOC Canadian Young Adults
Bibliographic record
Abstract
Black and Latinx individuals frequently experience ethnic-racial discrimination, a harmful stressor that leads to increased depressive symptoms and lower psychological well-being. Recently, scholars have argued that internalized racism, or negative beliefs about one’s racial group, may be the most harmful form of racism (Seaton, 2020). The adverse negative mental health effects of internalized racism have been extensively documented, however, internalized racism’s impact on aspects of psychological well-being (e.g., self-esteem) and development of a strong ethnic-racial identity, a developmental milestone among people of colour (Umaña-Taylor et al., 2014), have not been as well-documented – particularly in a longitudinal fashion. Further, familial social support is a known protective factor against the adverse effects of in-person racism, however, less research has documented its ability to protect against the stress of internalized racism. The present pre-registered study addresses these gaps by examining the short-term longitudinal (6-month) impacts of internalized racism on self-esteem and ethnic-racial centrality. Additionally, we will examine the main effect and the moderating influence of familial social support to determine whether it can mitigate the hypothesized adverse effects of internalized racism on our outcomes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".