Bibliographic record
Abstract
Corporations are consumers of treaty law. In this Article, I empirically examine three biodiversity treaty regimes-the Convention on Biological Diversity, Ramsar Convention, and World Heritage Convention-to demonstrate that corporations implement or internalize treaty norms in a variety of ways that are not captured by the dominant model of treaty implementation-national implementation. As an exegetical model, I explore how corporations use biodiversity treaties as a source of private environmental standards. I focus on the interactions between mining and oil and gas companies and biodiversity treaties, as revealed through transactional documents, corporate reports, security law filings, and treaty secretariat reports. My central claim is that treaties provide a vital, but overlooked, point of interaction between intergovernmental environmental law and transnational law as developed by private actors. This article reveals that the gravitational pull of treaties on private actors is differentially experienced. The shadow of law (both national and international) works variably across different companies, different industries and different geographies. And the same companies that are 'dumbing down' treaty meanings in one context may be advancing tools that promote stronger and deeper implementation of these same treaty norms in another. While the empirical record is thus littered with inconsistencies and seeming contractions, one thing is clear: the implications of corporate channelling of treaty meanings and obligations are significant for international law far beyond the context of biodiversity conventions. Growing pressure to define acceptable standards of environmental and social behavior for companies is creating a robust market for "international standards"-a market for treaties.
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 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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.018 | 0.021 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.034 | 0.003 |
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".