Making Sense of Mitigation Used to Address Industrial Effects on \nWildlife in Canadian Environmental Assessments
Bibliographic record
Abstract
Within a given industrial project, adverse environmental effects are a likely occurrence. Current environmental sustainability doctrine in Canada suggests that adverse environmental effects need to be adequately addressed in order to be avoided or minimized. The Environmental Assessment (EA) process has been developed to provide a systematic means for effects analysis and to cultivate mitigation programs to offset adverse effects. However, the progress of EAs often leads to development of industry with inadequate regard for mitigation for wildlife and their habitats. To better understand the mechanisms of mitigation programs used to offset effects to wildlife in the Canadian EA process, I established three studies consisting of quantitative and qualitative methods of inquiry. In the first study, I interviewed mitigation experts on their use and perceptions of success of various mitigation programs. I found that programs used by experts in different occupation groups differ in terms of frequency of use. Further, the overall pattern for perception of success of mitigation programs remained consistent. Experts were hesitant to label any mitigation program as reliably successful in offsetting adverse environmental effects. Second, I examined the role of an informational tool in informing EAs and subsequent mitigation. Using a Strengths, Weaknesses, Opportunities, and Threats analysis, I evaluated the telemetry tool. I found that a specific set of support systems is needed to implement telemetry on a useful basis. Last, I used data from experts’ knowledge interviews to unearth trends in mitigation practices. I used this information to develop policy and operational recommendations for improving the Canadian EA process. I conclude this dissertation with a synthesis chapter that demonstrates the contributions of these studies, and provides suggestions for future research.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.023 | 0.011 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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 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".