Poverty, Pandemics, and Wildlife Crime
Bibliographic record
Abstract
The COVID-19 pandemic has caused a global recession and mass unemployment. Through reductions in trade and international tourism, the pandemic has particularly affected rural economies of tropical low- and middle-income countries where biodiversity is concentrated. As this adversity is exacerbating poverty in these regions, it is important to examine the relationship between poverty and wildlife crime in order to better anticipate and respond to the impact of the pandemic on biodiversity. To that end, we explore the relationship between poverty and wildlife crime, and its relevance in the context of a global pandemic. We examine literature from conservation, criminology, criminal justice, and social psychology to piece together how the various dimensions of poverty relate directly and indirectly to general criminal offending and the challenges this poses to conservation. We provide a theoretical framework and a road map for understanding how poverty alleviation relates to reduced wildlife crime through improved economic, human, socio-cultural, political, and protective capabilities. We also discuss the implications of this research for policy in the aftermath of the COVID-19 pandemic. We conclude that multidimensional poverty and wildlife crime are intricately linked, and that initiatives to enhance each of the five dimensions can reduce the poverty-related risks of wildlife crime.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".