Indigenous people and the COVID-19 pandemic: the tip of an iceberg of social and economic inequities.
Bibliographic record
Abstract
The outbreak of the novel COVID-19 that began in Wuhan, China, killed a Yanomami (an Amazonian tribe) adolescent on 9 April 2020, presumed to have been contracted from gold miners. Although the strong influence of environmental conditions such as place of residence and socioeconomic status on health or illness is irrefutable, scant attention is paid to the interconnectedness of people and how conditions that affect one group ultimately affect everyone globally. The COVID-19 pandemic is a reminder or wake-up call of how a more equitable distribution of money, power and resources at global, national and local levels benefits all. Even though indigenous peoples and other socioeconomically disadvantaged communities will likely bear the brunt of the pandemic, no one will be spared its pervasive health, social, economic and political consequences. Indigenous peoples are ethnic groups who are the original or earliest known inhabitants of a particular geographic area. They are a heterogeneous group with thousands of culturally distinct communities, and numbers approximating 370 million in over 90 countries.1 Indigenous peoples comprise about 2% of the US population (6.8 million), 5% of the Canada’s population (1.7 million), 3% of the Australia’s population (>750 000) and there are about 32 million in South America, the majority in Peru. The epidemiological and social-ecological models are useful for understanding the uneven and disproportionate impact of COVID-19 on indigenous populations. The pandemic …
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".