Critical consciousness and anti‐racist action as rooted in family processes
Bibliographic record
Abstract
Abstract Racially ethnically marginalized communities in the United States are exposed to structural and interpersonal forms of racism that have harmful effects on their health, wealth, education, and employment (Centers for Disease Control and Prevention, Racism and Health. https://www.cdc.gov/minorityhealth/racism‐disparities/index.html , 2021). Although a plethora of research exists outlining these harmful effects, research examining how youth from diverse backgrounds effectively combat racism is lacking. Emerging research demonstrates that families may play a key role in the development of critical consciousness and participation in anti‐racist actions (Bañales et al., Journal of Social Issues, 2021, 77, 964; Blanco Martinez et al., American Journal of Community Psychology, 2022, 70, 278; Lozada et al., Journal of Black Psychology, 2017, 43, 493). Yet, many key family processes have not been examined in relation to youth development of anti‐racist practices. The current study included a sample of 327 racially ethnically diverse emerging adults (Mage = 18.80, SD = 1.28, range = 18–25), and explored the association between ethnic–racial socialization (cultural socialization, preparation for bias) and youth critical consciousness (reflection, motivation, action) and anti‐racist (interpersonal, communal, political change) actions, and how familism values impact these associations. Results found that ethnic–racial socialization was positively associated with all aspects of critical consciousness and anti‐racist actions. Results also found that familism significantly interacted with ethnic–racial socialization to predict some aspects of critical consciousness and anti‐racist actions, but not others. Implications of the findings and future research directions 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| 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".