The Need for Shared Nomenclature on Racism and Related Terminology in Psychology
Bibliographic record
Abstract
With the increased desire to engage in antiracist clinical research, there is a need for shared nomenclature on racism and related constructs to help move the science forward. This article breaks down the factors that contributed to the development and maintenance of racism (including racial microaggressions), provides examples of the many forms of racism, and describes the impact of racism for all. Specifically, in the United States, racism is based on race, a social construct that has been used to categorize people on the basis of shared physical and social features with the assumption of a racial hierarchy presumed to delineate inherent differences between groups. Racism is a system of beliefs, practices, and policies that operate to advantage those at the top of the racial hierarchy. Individual factors that contribute to racism include racial prejudices and racial discrimination. Racism can be manifested in multiple forms (e.g., cultural, scientific, social) and is both explicit and implicit. Because of the negative impact of racism on health, understanding racism informs effective approaches for eliminating racial health disparities, including a focus on the social determinants of health. Providing shared nomenclature on racism and related terminology will strengthen clinical research and practice and contribute to building a cumulative science.
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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 | 0.241 | 0.204 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.014 | 0.008 |
| Science and technology studies | 0.016 | 0.176 |
| Scholarly communication | 0.025 | 0.041 |
| Open science | 0.007 | 0.035 |
| Research integrity | 0.013 | 0.053 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".