Towards linking environmental law and science
Bibliographic record
Abstract
Gaps between environmental science and environmental law may undermine sound environmental decision-making. We link perspectives and insights from science and law to highlight opportunities and challenges at the environmental science–law interface. The objectives of this paper are to assist scientists who wish to conduct and communicate science that informs environmental statutes, regulations, and associated operational policies (OPs), and to ensure the environmental lawyers (and others) working to ensure that these statutes, regulations, and OPs are appropriately informed by scientific evidence. We provide a conceptual model of how different kinds of science-based activities can feed into legislative and policy cycles, ranging from actionable science that can inform decision-making windows to retrospective analyses that can inform future regulations. We identify a series of major gaps and barriers that challenge the successful linking of environmental science and law. These include (1) the different time frames for science and law, (2) the different standards of proof for scientific and legal (un)certainty, (3) the need for effective scientific communication, (4) the multijurisdictional (federal, provincial, and Indigenous) nature of environmental law, and (5) the different ethical obligations of law and science. Addressing these challenges calls for bidirectional learning among scientists and lawyers and more intentional collaborations at the law–science interface.
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 | 0.105 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.007 |
| Science and technology studies | 0.012 | 0.090 |
| Scholarly communication | 0.036 | 0.068 |
| Open science | 0.004 | 0.032 |
| Research integrity | 0.018 | 0.023 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".