Local Capacity for Energy Transition in Northern and Indigenous Communities: Analysis of Gwich’in Communities in Northwest Territories, Canada
Bibliographic record
Abstract
Introducing local renewable energy solutions into the fossil fuel dominated energy mix of many northern and off-grid Indigenous communities has the potential to create new socio-economic opportunity and address historical energy injustices. However, energy systems are comprised not only of technology and infrastructure but also the communities who generate, use, and benefit from energy. The design of local energy systems that are community appropriate thus requires an understanding of a community’s socio-technical capacity, coupled with an understanding of the social processes that stimulate and sustain transitions and the longer-term, desired outcomes from local energy. This paper explores the socio-technical capacity for renewable energy transitions in northern and Indigenous communities, based on a case study of four Gwich’in communities in the Northwest Territories, Canada. Results show that the foundational attributes of socio-technical capacity for energy transition in northern communities are interconnected, and strengths or challenges in one area often reflect strengths or challenges in another. Several capacity strengths already exist to support energy transition, including community energy values inclusive of community vision and the embedded and transferable skillsets of communities, coupled with next generation leaders. In turn, there are areas where significant capacity building is required, including supports for local energy champion(s) and enabling inter-local energy networks. Results also demonstrate that recent scholarly literature regarding local capacity for community energy does not tightly align with, or reflect the nuances of, energy transition needs in northern and Indigenous communities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".