Safe, sustainable, legal use and trade in wild species: testing a new five-dimensional sustainability assessment
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 Kunming Montreal Global Biodiversity Framework, adopted by the UN Convention on Biological Diversity in 2022, sets ambitious targets to ensure that the use, harvesting and trade of wild species is sustainable, safe and legal. While the definition of 'sustainable' is traditionally inclusive of ecological, social, and economic dimensions, many practically applied standards and regulations often exclude non-ecological perspectives such as human health and animal welfare. Recognising the challenge of assessing sustainability in a comprehensive, but accessible, way, a five-dimensional sustainability assessment framework (5DSAF) was developed, explicitly focusing on social, ecological, economic, animal welfare, and human health dimensions of sustainability. This paper documents the experiences of applying and testing the 5DSAF in multiple species use examples: geographically, by different sectors, and socio-economically. Its application in the United Republic of Tanzania (game meat industry), in South Africa (game meat sector), in Indonesia (reticulated python skins), and in Zimbabwe (Nile crocodile) is discussed. It proposes the steps for the future adaptations, and application of 5DSAF beyond the initial case studies aiming to assist conservation practitioners, policymakers, as well as indigenous peoples and local communities and private sector actors to demonstrate that the use of wild animal species and products is safe, legal and sustainable and, meeting the objectives of One Health approach, and where it is not, to identify the necessary improvements that need to be made.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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