Acting on Climate Change: Solutions from 60 Canadian Scholars
Bibliographic record
Abstract
Since 2013, United Nations Secretary-General Ban Ki-moon has been urging countries around the world to adopt ambitious climate change policies so as to avoid a global temperature increase of more than 2oC during this century. Answering this call, we formed the Sustainable Canada Dialogues, an initiative that mobilizes over 60 researchers from every province, working to identify a possible pathway to a low-carbon economy in Canada. Our position paper, Acting on Climate Change: Solutions from Canadian Scholars, launched in March 2015, identifies ten policy orientations illustrated by actions that could be immediately adopted to kick-start Canada’s necessary transition to a low-carbon economy and a sustainable society. Scholars from Sustainable Canada Dialogues unanimously recommend putting a price on carbon. Besides putting a price on carbon, Acting on Climate Change: Solutions from Canadian Scholars examines how Canada can reduce its greenhouse gas emissions (GHG) by (1) producing electricity with low-carbon-emissions sources; (2) modifying energy consumption through evolving urban design and transportation advancements; and (3) linking the transition to a low-carbon economy with a broader sustainability agenda, through the creation of participatory, well-coordinated, and open governance institutions that engage the Canadian public. Our proposals take into account Canada’s assets and are based on the well-accepted “polluter pays” principle.
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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 | 0.025 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.064 | 0.033 |
| Scholarly communication | 0.022 | 0.007 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.014 | 0.015 |
| Insufficient payload (model declined to judge) | 0.009 | 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".