Bibliographic record
Abstract
A series of multilateral environmental agreements (MEAs), such as the 1973 Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES), the 1987 Montreal Protocol on Substances that Deplete the Ozone Layer and the 1989 Basel Convention on the Control of Transboundary Movements of Hazardous Wastes and Their Disposal, follow policies and regulatory approaches that foresee restrictions on and supervision of international trade and/or transboundary movements of controlled commodities. These are crucial to fulfill the regimes’ underlying objectives of environmental protection. However, the inevitable corollary of such regulatory measures are transboundary black markets that pose a serious threat to these regimes’ effectiveness and, thus, to international environmental law. This paper appraises the distinctive ways in which a sample of key MEAs involved in the fight against transnational environmental crime – the Montreal Protocol, the Basel Convention and CITES – are addressing issues of illegality and criminality. Four conclusions are drawn. First, MEAs that face significant compliance issues due to emerging black markets in environmentally sensitive commodities have adopted a strategy of coordination and cooperation to increase their respective effectiveness. Second, inter-MEA coordination has furthered the significance of global and regional enforcement networks of practitioners as de facto norm-setting agents that have deeply influenced the normative development and implementation of MEAs. Third, this inter- and transnational process of cooperation has brought about a gradual criminalization of illegal trade in environmentally sensitive commodities. Fourth, and lastly, the evolution that has been highlighted in this paper clearly hints at an increasing awareness of TEC for environmental regime effectiveness and for the discrete emergence of a body of transnational environmental criminal law.
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.
How this classification was reachedexpand
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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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; both teacher heads agree on what is shown here.
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".