The relationship between environmental agreements and environmental impact assessment follow-up in Saskatchewan's uranium industry
Bibliographic record
Abstract
Environmental Impact Assessment (EIA) is a planning process used to predict, assess, mitigate, and monitor the potential environmental and social impacts that may be associated with a proposed development project.Essential to the efficacy of EIA is follow-up -a post-decision process that attempts to understand EIA outcomes and provides feedback on project development and learning processes to improve environmental management practices.While considerable literature on follow-up related themes exists, the actual implementation and engagement of all stakeholders involved with follow-up in post-consent decision stages lacks or is not done well.That being said, in northern Canada, and in the mining sector in general, much of this postdecision activity is occurring under a new institutional arrangement: privatized communityindustry Environmental Agreements and associated community-based monitoring programs.Based on a case study of follow-up in northern Saskatchewan's uranium mining industry, this thesis examines both the institutional development of EIA follow-up and the role and contribution of community-based Environmental Agreements to EIA follow-up and impact management practices.This thesis adopted a manuscript-style format; both utilized a combined methodology of document review and semi-structured interviews.The first manuscript focuses on the institutional development of follow-up in the northern Saskatchewan uranium mining industry, giving context to the current situation.Results demonstrate that follow-up in Saskatchewan's uranium industry has transformed and is characterized by four themes ranging from little or no follow-up to a new system that now includes a participatory yet privatized process based on privatized agreements.Results suggest that follow-up has evolved to a current emphasis on environmental management incorporating a 'community-centric' approach, recognition of socioeconomic issues in monitoring programs, and an increased community and I thank my supervisor, Dr. Bram Noble for all the guidance, knowledge, patience, and advice that was provided to me over the past two years.Bram, your support was integral for the completion of this thesis -thank you.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".