Navigating a global crisis: impacts, responses, resilience, and the missed opportunity of African protected areas during the COVID-19 pandemic
Bibliographic record
Abstract
Protected areas (PAs) serve as key institutions for biodiversity conservation. Therefore, ensuring their long-term resilience in the face of adversity is essential. Using resilience thinking we investigate the institutional resilience of PAs across Sub-Saharan Africa. Specifically, we examine how the managers of 50 terrestrial PAs across 17 countries responded to the COVID-19 pandemic and identify factors that facilitated or hindered their capacity to respond. We show that although most PAs were negatively affected by COVID-19, these impacts varied heavily in magnitude and duration across contexts. Many of these impacts had not been addressed, with some response attempts falling short or resulting in unintended consequences. Funding gaps, lack of agency, and a lack of resilience-thinking appeared as barriers to the PAs’ capacity to respond. Coping responses were the most common type used to navigate the crisis, whereas adaptive and transformative responses were rare. We interpret such predominant focus on short-term, coping responses as a sign of institutional resistance rather than resilience, and as a missed opportunity for transformation. We advocate for collective documentation and critical reflection on the effects and experiences of PAs emerging from COVID-19 and other shocks and conclude by emphasizing the need to mainstream resilience-thinking in conservation governance and management.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".