Bibliographic record
Abstract
This article seeks to develop the role law could play in contributing to the achievement of ecosystem resilience. Therefore, adopting Aldo Leopold’s view of conservation, by which the first step should be to understand nature, this article will begin with a brief explanation of the ecological background to the concept of ecosystem resilience. Next, the article will consider Aldo Leopold’s land ethic in order to discuss the values we should look for when implementing conservation for resilience. Regarding those values and concepts, the following part of the article will be dedicated to consolidating and contextualizing the legal principle. In order to carry out a more detailed analysis about how the principle of resilience can be pursued in the application of the law, this article will focus on certain sectors of environmental law and policy making. Those sectors are: adaptive governance, adaptive management, environmental impact assessment, land use and climate change adaptation, and market mechanisms for conserving ecosystem services. The article will be based on cases from different parts of the world. As the adoption of the concept of resilience by law seems to be incipient in the jurisdictions of most countries, such case studies will be helpful to any jurisdiction in the world where this concept is still not effective.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.050 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".