Biodiversity Protection through Networks of Voluntary Sustainability Standard Organizations?
Bibliographic record
Abstract
This paper explores the potential for voluntary sustainability standards (VSS) organizations to contribute to policy-making on biodiversity protection by examining their biodiversity policies, total standard compliant area, proximity to biodiversity hotspots, and the networks and partnerships they have in place that can support policy-making on biodiversity protection. The analysis undertaken is based on Social Network Analysis data, in combination with information from the International Institute for Sustainable Development (IISD) Standards and Biodiversity Review and the International Trade Centre (ITC) Standards Map on the focus and operation of VSS organizations. The significance of agriculture-focused private governance for global biodiversity policy and their relationship towards other forms of nongovernmental, governmental, and inter-governmental biodiversity policy are examined and described. We argue that, at present, a number of key agriculture-focused VSS organizations are important policy actors to address biodiversity because of their elaborate biodiversity policies, total compliant areas, and proximity to biodiversity hotspots. However, at present, most of these VSS organizations have relatively few ties with relevant governmental and inter-governmental biodiversity policymakers. The actor composition of their inter-organizational networks currently reflects a focus on nongovernmental rather than governmental organizations while substantively they focus more on development than on environmental protection issues.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".