Indigenous Peoples and the COVID-19 Pandemic: Learning, Preparedness, Challenges and the Way Forward
Bibliographic record
Abstract
The United Nations Declaration on the Rights of Indigenous Peoples (UNDRIP), in 2007 and the Philippines’ Indigenous Peoples Rights Act (1997) both recognize that access to education is a key component of Indigenous self-determination. Rushed responses in the early onset of the COVID-19 pandemic have, however, illuminated the tensions between contemporary statecraft and these ostensibly inalienable human rights. Employing quantitative and qualitative research methods, this study assesses the degree to which Indigenous Peoples (IPs) and Indigenous Cultural Communities (ICCs) in the Nueva Ecija region of the Philippines considered themselves prepared for the technologically dependent learning modalities that dominate the post-COVID educational landscape. While we recommend that government bodies and educational institutions work with IPs/ICCs to address longstanding inequalities, we also draw attention to how Indigenous knowledge contains key insights into contending with ongoing and future pandemics along with other existential crises of universal concern.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".