Indigenous Peoples and the COVID-19 Social Amelioration Program in Eastern Visayas, Philippines: Perspectives from Social Workers
Bibliographic record
Abstract
Amid the COVID-19 response, Indigenous Peoples suffer disproportionately and are especially at risk of being left behind in government responses due to the various inequalities they face. This paper discusses the treatment of Indigenous Peoples in the Philippines government’s COVID-19 policies and programs, and examines the implementation of the Social Amelioration Program (SAP), and its impact, or lack thereof, in the lives of Indigenous Peoples. This paper used a combination of secondary data from government policies and news articles, and primary data from ten rapid ethnographic interviews with social workers and SAP implementers from the regional social welfare agency of Eastern Visayas. We conducted a preliminary analysis on the various issues surrounding the SAP implementation as well as steps taken, or lack thereof, in making the program more inclusive and responsive to the plight of Filipino Indigenous Peoples in the region - a hazard prone area of the country. This essay is divided into three parts. The first illustrates the virus outbreak in the country and the challenges Indigenous Peoples face during the pandemic. The second discusses the policy that created the SAP and issues surrounding it. The last one highlights the local social workers’ perspectives and recommendations on how the government could better contribute to the social development as well as general wellbeing of Indigenous Peoples during and after 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 | 0.004 | 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 | 0.020 | 0.011 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".