Racism and ethnic discrimination among Indigenous Arctic populations: methods, data, definitions. A scoping review
Bibliographic record
Abstract
OBJECTIVES: Racism and ethnic discrimination are global health issues, but the extent and effects on Indigenous Peoples in the Arctic region are still poorly understood. By investigating the methods, data sources, and definitions used in articles examining racism and ethnic discrimination among Indigenous peoples in the Arctic between 2008 and 2021 this review aims to create a solid foundation for future research. DESIGN: We conducted a search across multiple databases, including PubMed, PsycInfo, Web of Science, Scopus, and the Cochrane Review. Our search criteria included: Indigenous groups, racism or ethnic discrimination, and Arctic regions. After removing off-topic articles, two researchers reviewed the remaining articles against predefined eligibility criteria. RESULTS: The research field is expanding, but a significant portion of Arctic Indigenous peoples remains underrepresented. Predominant research methods include questionnaires, interviews, and case studies, often derived from large cross-sectional studies. Self-reported responses to questions about ethnic discrimination and racism are the primary research method, while some articles involve researchers subjectively evaluating data to determine what qualifies as racism or ethnic discrimination. Reaching a consensus on the definitions of ethnic discrimination and racism is challenging, with definitions ranging from negative, unfair, or differential treatment to broader, structural perspectives. Approximately half of the articles lack clear definitions. CONCLUSION: There is a notable difference in terminology, where racism as a term is more used in Canada/US while, ethnic discrimination is more predominant in the Nordic countries. Despite these differences, the scales used to measure racism or ethnic discrimination show significant similarities. A large part of the investigated articles emphasize interpersonal discrimination. An emerging perspective after 2016 views racism/ethnic discrimination as something that produces inequalities between racial or ethnic groups and upholds or creates systems of privilege and oppression. Research consistently highlights the importance of considering local contexts of racism, ethnic discrimination and oppression.
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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 | 0.024 | 0.090 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.029 | 0.022 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".