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
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.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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