Strategic Impact Assessment on Climate Change in Project and Regional IA
Bibliographic record
Abstract
The following post has been prepared jointly with Professor Bob Gibson at the University of Waterloo, and Karine Péloffy at the Centre Québécois du Droit de L’environnement. The work is a small part of a broader research collaboration on the integration of climate change into EA funded by the Metcalf Foundation and SSHRC.\nFor decades now, successive the Canadian federal governments have been making international and domestic commitments to climate change mitigation. So far, the record of achievement has been poor. Among the signs of inattention to effective action is that no Canadian government has made a serious attempt to define the implications of our broad commitments for planning and decision making about particular undertakings. As a result, we have been assessing and approving major projects without informed evaluation of whether or not their attributable lifetime greenhouse gas (GHG) emissions would be in line with meeting our commitments.\nThankfully, that may be about to change. In its June 2017 discussion paper on environmental assessment process reform, the current federal government proposed an approach to cumulative effects issues that includes “[c]onducting strategic assessments that explain the application of environmental frameworks to activities subject to federal oversight and regulation, starting with one for climate change.” Undertaking such strategic assessments has been a prime recommendation of many participants in the federal assessment processes reform exercise.
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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 | 0.020 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".