Racial justice allyship requires civil courage: A behavioral prescription for moral growth and change.
Bibliographic record
Abstract
In racialized societies, race divides people, prioritizes some groups over others, and directly impacts opportunities and outcomes in life. These missed opportunities and altered outcomes can be rectified only through the deliberate dismantling of explicit, implicit, and systemic patterns of injustice. Racial problems cannot be corrected merely by the good wishes of individuals-purposeful actions and interventions are required. To create equitable systems, civil courage is vital. Civil courage differs from other forms of courage, as it is directed at social change. People who demonstrate civil courage are aware of the negative consequences and social costs but choose to persist based on a moral imperative. After defining allyship and providing contemporary and historical examples of civil courage, this paper explains the difficulties and impediments inherent in implementing racial justice. To enable growth and change, we introduce ten practical exercises based on cognitive-behavioral approaches to help individuals increase their awareness and ability to demonstrate racial justice allyship in alignment with valued behaviors. We explain how these exercises can be utilized to change thinking patterns, why the exercises can be difficult, and how psychologists and others might make use of them to expand the capacity for civil courage in the service of racial justice. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.028 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| 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".