Empowering Churchill: Exploring Energy Security in Northern Manitoba
Bibliographic record
Abstract
To those living in Churchill, Manitoba, having power means much more than being able to turn on the lights. Using Churchill as a case study, we examine how local context can improve the suitability of energy security definitions for communities in northern Canada. Churchill is an isolated northern municipality with no road access but is connected to the electrical grid. Energy consumption data were collected from utility providers and organized into a community energy profile. Semi-structured interviews (n = 23) and a community workshop (n = 12) identified challenges, opportunities, and a vision for Churchill’s energy system. High per capita energy consumption, especially of transportation (jet fuel) and heat (electricity and propane) sources dominate Churchill’s energy profile. The reliance on air travel and need for heating are realities that define energy systems in the North. Participants expressed desire for increased use of renewables and improved energy efficiency. Churchill is reliant on external sources of power and there is a need for agency and local decision making. Jurisdictional realities and the community’s desire for consideration of local context mean energy security definitions should take a regional approach. Recognizing these findings, we propose a new definition of energy security that fits the circumstances and desires of Churchill and the North.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.028 | 0.007 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".