Bibliographic record
Abstract
While governments assert that Canada is a world leader in sustainability, Unnatural Law provides extensive evidence to refute this claim. A comprehensive assessment of the strengths and weaknesses of Canadian environmental law, the book provides a balanced, critical examination of Canada’s record, focusing on laws and policies intended to protect water, air, land, and biodiversity. Three decades of environmental laws have produced progress in a number of important areas, such as ozone depletion, protected areas, and some kinds of air and water pollution. However, Canada’s overall record remains poor. In this vital and timely study, David Boyd explores the reasons why some laws and policies foster progress while others fail. He ultimately concludes that the root cause of environmental degradation in industrialized nations is excessive consumption of resources. Unnatural Law outlines the innovative changes in laws and policies that Canada must implement in order to respond to the ecological imperative of living within the Earth’s limits. The struggle for a sustainable future is one of the most daunting challenges facing humanity in the 21st century. Everyone – academics, lawyers, students, policy-makers, and concerned citizens – interested in the health of the Canadian and global environments will find Unnatural Law an invaluable source of information and insight. For more information on Unnatural Law visit David Boyd's site, www.unnaturallaw.com.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".