Submission to Canada’s public engagement on the 2035 greenhouse gas emissions reduction target
Bibliographic record
Abstract
Canada has a long record of failing to achieve its international climate commitments. These include the UN Framework Convention on Climate Change (UNFCCC) collective goal of returning to 1990 levels of greenhouse gas emissions by 2000, Canada’s legally binding Kyoto Protocol target of 6% under 1990 levels by 2012, and its political target announced at the Copenhagen conference of achieving a 17% reduction in emissions from 2005 levels by 2020. Rather, national emissions of greenhouse gases (GHGs) rose 21% between 1990 and 2020, from 602 to 730 megatons of carbon dioxide equivalent (MtCO2e) per year.1 The ongoing public engagement on the 2035 greenhouse gas emissions reduction target offers a momentous opportunity to that Canadian society cannot afford to ignore if it is ever going to close the gap between its international commitments and its actions. Building on my research at Toronto Metropolitan University as member of the International Law & Global Justice Initiative (ILGJ), my previous post-doctoral research at the Cambridge Centre for Environment, Energy and Natural Resource Governance (C-EENRG) and a recent piece that I published in The Conversation, I would like to provide the following three submissions to your consultation. March 27th, 2024
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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 | 0.012 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.028 | 0.007 |
| Scholarly communication | 0.016 | 0.003 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.034 | 0.023 |
| Insufficient payload (model declined to judge) | 0.052 | 0.014 |
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".