Tropical forest environments provide insurance against COVID-19
Bibliographic record
Abstract
Research prior to the COVID-19 pandemic has shown that the rural poor often turn to wild resources to cope with adverse shocks. We report on the first study addressing natural insurance against health shocks during the COVID-19 pandemic, focusing on riverine communities without road access in the Peruvian Amazon. We consider the most devastating shock people may experience, the death of a close family member. Using data from an in-person survey of almost 4000 households in 235 randomly selected communities before the pandemic as baseline, we conducted telephone surveys with over 400 communities during the early phase of the pandemic. We found that before the pandemic, forest peoples relied on game and non-timber forest products to cope with mortality, whether in their own household or their community. Once COVID-19 arrived, people reduced their reliance on hunting and resorted instead to fishing. These patterns were differentiated by gender and indigeneity. Tropical forest environments, which include also aquatic habitat, provide vital insurance against mortality, but just how may be altered during a pandemic. These novel findings have important implications for research and policies on forest conservation and pandemics.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".