Bibliographic record
Abstract
The global spread of COVID-19 is quickly exacerbating existing racial and economic disparities, and in its wake, revealing the spatial dynamics of health and interlocking social inequalities that burden marginalized communities. Among Indigenous Peoples, increased risk of exposure is linked to the enduring settler-colonial logics of Indigenous elimination and present-day mistreatment of tribal communities by settler-states that occupy their lands. Specifically, Indigenous communities face social problems such as access to quality, affordable healthcare, sustainable public infrastructure, opportunities for economic self-sufficiency, nutritious food, and clean water. Relatedly, Indigenous cultures and languages are often stigmatized and othered, which may dissuade some Indigenous Peoples from seeking out medical and social services when in need. Indigenous Peoples are collectively identified as those communities that lived on and cared for a particular land base before the arrival of foreign settlers, inhabitants that routinely threatened Indigenous communities with death, disease, and destruction. Despite those efforts, there are upwards of 400–500 million Indigenous Peoples living around the world today. These communities nourish distinct languages, cultural perspectives, legal systems, and actively resist threats to their knowledge systems from settler societies. In 2020, COVID-19 amplified these threats across the globe. In the Americas, for example, 40 percent of Indigenous Peoples do not have access to conventional healthcare (Cevallos and Amores, 2009) and 73 percent of Canada’s First Nations’ water systems are at risk of contamination (Council of Canadians, 2020).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.015 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".