Sustainability Assessment of the Impact Assessment Act of 2019
Bibliographic record
Abstract
Environmental impact assessment (EIA or IA) has been around since the 1970’s and is a governmental process that evaluates the impacts of a project, policy, program, plan, and other initiatives. The process evaluates these initiatives to determine impacts, and explores associated mitigation techniques, alternative solutions, or stopping the initiative altogether. However, is the process itself sustainable? Does it produce sustainable decisions? Hence the purpose of this study is to evaluate the Canadian EIA process for sustainability, specifically the recent iteration of federal EIA law: the Impact Assessment Act of 2019 (IAA 2019, or ‘the Act’). This study combines the methods of policy evaluation, sustainability assessment, and Next-Generation environmental assessment to evaluate the 1AA 2019 against 3 sustainability criteria: 1. Strategic Assessment, 2. Public Participation, and 3. Indigenous Peoples. The main research question is: Is the IAA 2019 an effective instrument in embedding sustainability in Canada? The results of this evaluation found that the Act is a partially effective instrument in embedding sustainability in Canada. Some strengths found include usage of the term ‘meaningful participation’ throughout the statute, and a strong top-down tiering approach. Some gaps found include public participation provisions that do not directly link to racialized and marginalized groups, and weak linkages to the United Nations Declaration on the Rights of Indigenous Peoples.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".