Comparing Provincial EA: A Comparative Analysis of Five Canadian Provincial Approaches to Municipal EA
Bibliographic record
Abstract
The purpose of this major research paper is to examine the provincial environmental assessment processes within five Canadian jurisdictions for comparison using the EAOGRAM metric created by the IAIA. The paper focuses on provincial EA and its subsidiary practices in municipalities to explore provincial EA at the local level and on the scale of large provincial projects. \n \nEnvironmental Assessment is a well studied and utilized planning tool in Canada and much has been written on EA in each province individually and increasingly across jurisdictional lines. There is however, little written in terms of comparative analysis of these policies. That is, comparing jurisdictions side by side for commonalities and opportunities for improvement or to encourage a better understanding of how EA operates across Canada regionally. The diversity of approaches across Canada has garnered several attempts to standardize EA, however this has largely been unsuccessful due to the distribution of power in the provinces, the difficulty in administration, and the different needs of EA from each region.1 This paper will further an understanding of the differences and commonalities between provincial EA practices in a few of Canada's jurisdictions. The provinces are specifically British Columbia, Alberta, Saskatchewan, Manitoba and Ontario. The evaluation will utilize case studies from routine municipal projects, specifically road-extension projects to create a comparable baseline as it relates to municipal EA. The case studies presented are examples of typical road extension projects and thus reflect a comparable baseline by which to evaluate the projects. The evaluation will also employ the popular EAOGRAM developed by the International Association on Impact Assessment to evaluate each province on 10 criteria for effective EA practice. The provinces have been compared using this criteria by the IAIA in 1994, since then much of Canada's EA practices have changed, this paper will compare the 5 selected jurisdictions and contribute to our understanding of EA practice across provincial lines both municipally and provincially. \n \n1 Constitution Act, s. 92 and 92A, being Schedule B to the Canada Act 1982 (UK), 1982, c 11. 4
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.023 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".