Impacts of COVID-19 on Biodiversity Conservation and Community Networks at Kibale National Park, Uganda
Bibliographic record
Abstract
Conservation, like all aspects of society, was severely affected by the COVID-19 pandemic. Although there have been projections and speculations about impacts on conservation plans and actions, data about the extent of these impacts are sparse. We contribute evidence from a research field site in Kibale National Park, Uganda. Our analysis shows that many of the fears concerning the negative conservation impacts of COVID-19 were borne out. Long-term research projects were disrupted, affecting employment opportunities in the park. These effects percolated into the local communities, which reported high levels of financial stress and other negative impacts, such as increased rates of teenage pregnancy. People who were permanently employed at the park reported lower levels of financial stress. Also particularly concerning was the increase in poaching in the park due to a lack of food security. This research highlights an important path toward resiliency for research stations in the face of global crises, but it requires changes in funding duration and scope from granting agencies and governments. Operating differently than ecotourism, research field stations provide unique opportunities to build resilient conservation instruments and the results of this research can help guide policies to make research field stations more resilient.
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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 | 0.001 | 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 | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".