Attributes of Socio-Technical Baseline Capacities for Energy Transition in the North: Opportunities and Challenges for Gwich'in Communities, Northwest Territories
Bibliographic record
Abstract
Today’s societies confront significant challenges concerning historic energy systems, which are becoming increasingly vulnerable to the threat of climate change. Energy systems need to be adapted to create greater resilience for the future. However, ensuring long-term success in community energy development in the North requires more than building new projects – it requires understanding the local socio-technical capacity to design, implement, and maintain renewable energy projects. Consequently, the design of community-appropriate sustainable energy systems requires a socio-technical understanding of a community’s baseline capacity for energy transition. In 2018, through the 2030 Energy Strategy: A Path to More Affordable, Secure, and Sustainable Energy in the Northwest Territories, the Government of Northwest Territories encouraged local or community level renewable energy development within the territory. Communities in the territory considering their energy futures include the Gwich’in communities of Aklavik, Fort McPherson, Inuvik, and Tsiigehtchic. The challenge, however, is that there is limited research on what a socio-technical baseline capacity profile for a remote northern Indigenous community involves. Therefore, the purpose of my research is to understand the socio-technical baseline capacity for energy transition in Gwich’in communities. The initial objective consisted of developing a conceptual framework for socio-technical baseline capacity profiles. The rapid assessment framework is conceptualized based on the energy context of rural and remote regions in the North and informed by recent energy planning grey literature and scholarship. The conceptual framework was then applied with the four partner communities, based on semi-structured interviews with community members, Gwich’in leaders, intermediary organizations, and energy sector representatives, identifying key strengths, challenges, and regional trends across the partner communities. Results identify several key capacity opportunities and challenges for energy transition, emphasizing the importance of community-to-community capacity building and long-term capacity building within the region. I conclude with a discussion of the research key findings – identifying diverging perspectives, the importance of inter-local energy networks, the intertwined nature of the attributes of the framework, and future research needs.
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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 | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".