Concrete Lessons: Policies and Practices Affecting the Impact of COVID-19 for Urban Indigenous Communities in the United States and Canada
Bibliographic record
Abstract
Throughout the Americas, most Indigenous people move through urban areas and make their homes in cities. Yet, the specific issues and concerns facing Indigenous people in cities, and the positive protective factors their vibrant urban communities generate are often overlooked and poorly understood. This has been particularly so under COVID-19 pandemic conditions. In the spring of 2020, the United Nations High Commissioner Special Rapporteur on the Rights of Indigenous Peoples called for information on the impacts of COVID-19 for Indigenous peoples. We took that opportunity to provide a response focused on urban Indigenous communities in the United States and Canada. Here, we expand on that response and Indigenous and human rights lens to review policies and practices impacting the experience of COVID-19 for urban Indigenous communities. Our analysis integrates a discussion of historical and ongoing settler colonialism, and the strengths of Indigenous community-building, as these shape the urban Indigenous experience with COVID-19. Mindful of the United Nations Declaration on the Rights of Indigenous Peoples, we highlight the perspectives of Indigenous organizations which are the lifeline of urban Indigenous communities, focusing on challenges that miscounting poses to data collection and information sharing, and the exacerbation of intersectional discrimination and human rights infringements specific to the urban context. We include Indigenous critiques of the implications of structural oppressions exposed by COVID-19, and the resulting recommendations which have emerged from Indigenous urban adaptations to lockdown isolation, the provision of safety, and delivery of services grounded in Indigenous initiatives and traditional practices.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.036 | 0.018 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".