Bibliographic record
Abstract
This book presents a new method for measuring the effectivity of national and international environmental law. It took four years of research and experimentation to develop a way to construct evidence-based legal indicators. The existing environmental indicators evaluate only statistical, scientific or economic data. With legal indicators, governments, parliaments and other public and private actors, including environmental NGOs, will be able to assess accurately and concretely, on a scientific basis, what the gaps, progress and setbacks in the implementation of international conventions and national laws are. The legal indicators will also serve as innovative tools for decision-making, in particular to carry out legislative reforms in full knowledge of the facts and not blindly, as well as to avoid regressions in environmental law. The mathematical method used makes it possible, through a questi onnaire addressing all the legal and institutional stages of the application of legal texts, to provide data highlighting both the points to be improved and the strengths of the application of the law. This essay is an update of a first book published in 2018 by the Institut de la Francophonie pour le d�veloppement durable. It is the result of a partnership between the International Centre for Comparative Environmental Law and the Normandy Chair for Peace.
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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".