The Need for Shared Nomenclature on Racism and Related Terminology in Psychology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it