Catherine McKenna, Former Minister of Environment and Climate Change, Canada: International and National Role in Climate Policy
Bibliographic record
Abstract
Climate leadership in Canada is highly politicised due to the significant role that fossil-fuel dominated energy industries play in the federation’s regional political economies – totalling nearly 12% of GDP at CAD309 billion in 2022 ( Natural Resources Canada (NRCan), 2024 , p. 7; Carter, 2020 ). Canada is the second largest country, by area, in the world with the third highest GHG intensity per capita in the OECD (after Australia and the United States), and fifth highest total emissions in the OECD ( OECD, 2023 ). The country’s geography, climate and culture of high energy use have led to significant challenges related to decarbonising transport, buildings and the energy sector, and this is without even scratching the surface of addressing the history and practice of colonisation and its effects on Indigenous (First Nations, Metis and Inuit) peoples. Canada’s rapidly expanding population consists of an ethnically diverse 40 million people in 2023, of whom the fastest growing and youngest segment is Indigenous peoples.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".