Developing a National Strategy for Climate Engineering Research in Canada
Bibliographic record
Abstract
Climate engineering (CE) is increasingly becoming an area of broad public policy interest within international and domestic climate policy discussions. In addition to receiving greater attention within regulatory contexts, there is a gradual shift toward greater support for nationally supported research programs on CE technologies and assessments. Despite the increased salience of CE, the issue has been largely absent from the Canadian public policy agenda. This paper argues that a national strategy for CE research ought to be developed as part of Canada’s broader climate strategy. At the centre of this strategy must be a commitment to ensuring a high level of public trust in the underlying science and a policy process that is open and responsive to public views. The development of a national CE research strategy is necessary because governance of CE cannot be undertaken in the absence of greater knowledge of CE technologies and their potential impacts. In addition, development of other climate responses, such as mitigation and adaption strategies, will need to be understood in light of the risks of CE, but also the risks associated with forgoing these technologies. As CE technologies become subject to increasing international oversight, the Canadian government needs to develop a greater understanding of these technologies as part of a coherent national position on CE.
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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 | 0.044 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.021 | 0.008 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".