Community Energy Planning as a Pathway Towards Reconciliation
Bibliographic record
Abstract
There is a growing demand for renewable energy production to contribute to achieving emissions reduction targets in the face of global warming. Indigenous communities across Canada are being called to contribute to the renewable energy sector and participate in collaborative energy developments. While cross-cultural collaborations are not new to the natural resource sector, there is an increasing need for improved practices to collaborate with Indigenous peoples, especially in the renewable energy sector. In collaboration with Eagle Lake First Nation, this research sought to understand the challenges and barriers to engaging in collaborative natural resource management, and determine how to improve cross-cultural engagement processes, with applications in the renewable energy sector. A literature and document review, interviews and community engagements were used to identify challenges and barriers, identify ideal engagement scenarios, and develop recommendations for enhancing cross-cultural engagement processes. This research contributed to developing the community perspectives portion of a community energy plan for Eagle Lake First Nation. In addition, the findings of this research indicate that cross-cultural collaborations in the renewable energy sector presents opportunity to address Reconciliation, while improving the standards and common practices to which engagements are held to. Recommendations for improved engagement practices are provided for First Nations communities, academics, industry and government collaborators.
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 | 0.016 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.017 | 0.013 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".