Sustainable Development and Underexplored Topics in Canada’s Energy Transition
Bibliographic record
Abstract
Canada’s energy system is undergoing a fundamental shift, which will change how Canadians produce and consume energy. The success of Canada’s energy transition will be influenced by the ability of energy practitioners to manage the tensions and trade-offs in a variety of topics. The purpose of this research was to identify topics that are relevant to Canada’s energy transition and to identify the concepts that energy practitioners are using to manage the tensions and trade-offs in these topics. According to in-depth interviews with Canadian energy practitioners in 2021, the two most important topics in Canada’s energy transition are climate change and reconciliation with Indigenous Peoples. In addition, according to a 2021 focus group with Canadian energy practitioners, three relevant and underexplored topics in Canada’s energy transition are environmental rights, a systemic reduction in energy consumption, and learning from the energy transition in other countries, notably, Germany. These three underexplored topics were studied by completing additional in-depth interviews in 2022 and 2023, and a causal loop analysis in 2023. This research suggests that the concepts of sustainable development and multi-level perspective are complementary, can increase understanding of important and underexplored energy transition topics, and can generate solutions to complex sustainability challenges.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.032 | 0.023 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".