Bibliographic record
Abstract
Legislated impact assessment requirements were first introduced over fifty years ago with the National Environmental Policy Act in the United States and have since spread to over a hundred and fifty jurisdictions around the world. The details have varied widely, reflecting the global diversity of socio-ecological and governance systems and associated issues, traditions, capacities, ambitions, and power structures. In 2015, Canada embarked on a task that no other country has attempted in recent years: fundamentally reconsidering how best to tackle environmental assessment. This review and revision process ended with the passage of the Impact Assessment Act (IAA) in 2019.\nThe Next Generation of Impact Assessment explores the evolution of the Canadian assessment process and evaluates the effectiveness of the IAA. Each chapter provides an in-depth analysis of the Act in regard to: the contents of the IAA regulations and guidance an assessment of these provisions based on the literature best practices related to the essential elements of impact assessment and federal government commitments made establishing any action needed to ensure effective implementation of the IAA \nThe book also investigates areas of concern for implementation of the Act and proposes areas of further reform. The authors apply their expertise by providing a comprehensive and detailed examination of various sections and provisions while considering essential components that should be included in the next generation of assessment law and policy. The authors conclude that the IAA has the potential to one day symbolize a breakthrough in the federal assessment process, and this text is an invaluable resource dedicated to the successful implementation of the Act and to its continuous improvement.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".