The psychological study of race, diversity, and culture: Foundational contributions of James M. Jones to modern theories of racism.
Bibliographic record
Abstract
The field of psychology has a history of harming racialized communities through the endorsement of scientific racism and the systematic silencing and erasure of dissenting voices. The field has a moral imperative to work collectively to create a future where the experiences, perspectives, and contributions of Black people are included and celebrated. Here, we contribute to centering Black voices by highlighting the scholarship of Professor James M. Jones, whose work on racial issues and diversity has had a profound impact. Our aim was twofold: (a) critically review foundational pieces of Jones' work and identify core themes and (b) discuss the impact of Jones' work on science and society, including areas for future research. Using various keyword strategies and in consultation with Professor Jones, we conducted exploratory and confirmatory searches using APA PsycInfo, EBSCOhost, and Google Scholar. We curated 21 pieces for review and identified six core themes: (a) racism as a universal context, (b) culture and context matter in situating historical and temporal narratives, (c) methodological limitations of psychological examinations of race, (d) doing diversity, (e) accepting divergent social realities, and (f) coping with oppression. Jones' systems-level analysis of racism provides a strong theoretical and analytical framework for the study of racial issues. Jones' impact and legacy extend far beyond the academe: as director of the Minority Fellowship Program and executive director of public interest at American Psychological Association, he has influenced generations of psychologists and paved a pathway for psychological science methods in social policy. (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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.011 |
| Insufficient payload (model declined to judge) | 0.001 | 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".