Examining racial, ethnic, and cultural diversity in occupational science research: Perspectives of persons of color
Bibliographic record
Abstract
Diverse sociodemographic identities, including race, culture, ethnicity, and gender, are important influences on one’s occupational patterns and choices. However, occupational science theories and research were originally driven by Western White middle-class researchers and conducted on White participants. With a focus on the Western context, we sought to identify areas for improvement in the delivery and conduct of occupational science research with considerations of race, ethnicity, culture, and occupation among underrepresented racial groups. A critical content analysis was conducted of empirical research undertaken in Western countries between 2015 and 2020 and published in the Journal of Occupational Science (JOS). This analysis asked (a) What is the stated positionality of first author? (b) What are the racial or ethnic orientations of research participants? and (c) Is there explicit discussion of a racial/ethnic phenomenon? The findings reveal a lack of scholarship on race, ethnicity, and culture. Many primary authors did not explicate their positionality in relation to the research topics and study participants. The findings reify that the current production of occupational science research continues to occur within a wider field of social relations that is characterized by the agendas, interests, and values of the dominant group. Informed by critical race theory, we urge occupational science academic journals and their contributing authors to commit to epistemological antiracism. We recommend making space for racialized perspectives; acknowledging how these identities affect engagement and choice of occupations; clarifying who regulates, narrates, and participates in occupational science research; and creating inclusive scholarly ecosystems.
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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 | 0.044 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.037 | 0.026 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".