Bibliographic record
Abstract
One of the objectives of early scholarship on the political economy was to inquire into sources of wealth, the impacts of wealth-generating activities on societies, and the potential roles of government both in promoting wealth-generation and aligning it to other societal interests. Contributing to this project today involves broadening it, or at least shifting the emphasis. To the interest in productive uses of land is added destructive exploitation of ecosystems and their components. In this vein, this paper explores the recent trajectory of international environmental law. In its early stages, this body of law focused on the rights and obligations of states, in a manner clearly inspired by a private law approach. This has shifted to a less formal, more politicised approach involving attempts to balance the competing interests of states. Legal rules are increasingly procedural, framing largely political processes in which the creation of elaborate regulatory regimes is negotiated. The framing of environmental regimes often depends very heavily on economic logic, as the decentralised structure of international society and international law mean that states must choose to join, remain committed to, and comply with the rules of international environmental regimes. One obvious manifestation of these trends is the move from enforcement (findings of breaches of rules, imposition of legal consequences) to managing compliance (removing obstacles to and creating incentives for adherence to rules). The foil to this economic approach has often been ethics rather than law, in the form, for example, of challenges to the perceived commodification of nature (e.g. bulk water exports or markets for emissions credits). Law has been marshalled in support of these ethical projects by way of creating new rights, such as human rights to environment and the investing of natural objects or ecosystems with rights, which have the possibly unintended effect of enhancing the importance of economic logic, as conflicts among vaguely defined rights need to be resolved. In this landscape, law often seems to be at the service of other logics or discourses; less clear is whether law has its own contribution to make within international environmental regimes and if so, what shape that contribution could take.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.019 | 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".