'IAIA10 Conference Proceedings' The Role of Impact Assessment in Transitioning to the Green Economy 30th Annual Meeting of the International Association for Impact Assessment
Bibliographic record
Abstract
The author's paper (with Leeder and Federico) "Environmental Assessment Crisis in Canada: Reputation versus Reality? " delivered at IAIA 2005, argued that environmental assessment (EA) in Canada, as administered by the federal government, is inefficient, frequently of poor quality and fails to meet its basic objective as a tool of sustainable development. The authors made a number of suggestions for potential change. Since that time, the Government of Canada has increasingly recognized these challenges and begun to make some improvements and changes in policy and legislation in an attempt to improve efficiency and certainty in process. Changes in policy have seen improved decision making in scoping and reducing the number of EAs for projects of low environmental consequences. Considerable further is needed to address concerns regarding the administration of the Canadian Environmental Assessment Act (CEAA), including duplication with other (e.g., provincial and territorial) jurisdictions, the challenges of self-assessment (by proponent government departments) and the pursuit of meaningless or unnecessary EA that leads to little improvement in environmental performance. This paper explores the key remaining issues and offers suggestions around what the federal government needs to do commencing with the parliamentary review of the legislation in 2010. This includes continued focus on improved administration and practices while pursuing the rationalization of EA, through the achievement of a national framework for adoption by all jurisdictions to minimize duplication, uncertainty, inefficiency and ineffectiveness. Background
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.020 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.118 | 0.062 |
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".