Strategic Impact Assessment on Climate Change in Project and Regional IA
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it