Bibliographic record
Abstract
Abstract Rapid, unpredictable ecological changes and the resulting instability that are characteristic of the Anthropocene call for a re-examination of the role of law in governing interactions between humans and ecosystems and facilitating adaptation to ecological change. The scope and scale of environmental change we are experiencing seem to call for a regulatory approach, namely forms of law that are designed to pursue well-defined material objectives, often through instruction rules designed to guide behavior to line up with those objectives. Such forms of law have a crucial role to play. However, the negligence principle at the heart of civil liability law is also capable of absorbing and circulating information about environmental risk and means of addressing it, and of translating that information from empirical to normative terms. The grounding of negligence in domestic civil liability law could be a serious obstacle to its effectiveness given the global, Earth system-wide nature of environmental degradation. However, the negligence principle increasingly operates through networks that traverse jurisdictional boundaries, as well as the boundaries between social systems. I propose such a network approach to analyze interactions between the negligence principle and corporate due diligence obligations embedded in domestic legislation and international texts such as the United Nations Guiding Principles on Business and Human Rights (UNGPs). One important result would be the imposition of expanded epistemic obligations on firms, which would in turn require their serious engagement with domestic, international, and transnational environmental and sustainability norms.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.039 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".