Examining racial, ethnic, and cultural diversity in occupational science research: Perspectives of persons of color
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
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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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 | 0.019 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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