“Consensus Building in Engagement Processes” for Reducing Risks in Developing Sustainable Pathways: Indigenous Interest as Core Elements of Engagement
Bibliographic record
Abstract
Abstract Canada is one of the top ten greenhouse gas (GHG) emitters in the world, and of the nation’s total, the province of Alberta was the biggest emitter primarily due to the fossil fuel industry and power generation. Alberta is currently facing a challenge to reduce GHG emissions in line with Canada’s obligations to meet Paris Agreement goals. Additionally, the oil sand deposits in Alberta are located on the traditional land of Indigenous communities; therefore, the development, regulation and consultation of this sector have a direct impact not only on emissions but also on the socioeconomic welfare of Indigenous communities. Thus, the transition towards a low-carbon pathway in the oil sand industry is closely connected to upholding the rights of Indigenous peoples. Meaningfully consulting with Indigenous peoples is essential when developing low-carbon pathways that impact the environment and wellbeing of the community. Failure to consult proposed changes with Indigenous communities can lead to risks for the government, the industry and the communities themselves. These risks have been prevalent in the current consultation process in Alberta which has led to litigation, creating mistrust between the government, industry and Indigenous community. Given this background, we present a Consensus Building in Engagement Processes framework that includes Indigenous consensus, knowledge, interests and rights as a focal point of a consultation process required for decision-making. The consultation process is presented within the context of land use decisions impacting a low-carbon future for oil sand development. The framework is based on seeking consensus from all parties involved and aims to help to reduce risks resulting from decisions that do not consider the interests and rights of communities most impacted by resource development or climate mitigation pathways.
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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 | 0.110 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.017 | 0.048 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.006 | 0.030 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".