Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it