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Record W7019964838

Indigenous Participation in Clean Energy Activities in Canada: Passive Participation or ‘Community Energy’?

2019· other· en· W7019964838 on OpenAlexaboutno aff

Bibliographic record

VenueYork University Digital Library (York University) · 2019
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousParallelsCollective actionVariety (cybernetics)Energy (signal processing)Clean energyClimate change mitigationAction (physics)
DOInot available

Abstract

fetched live from OpenAlex

The trend toward bottom-up energy action through community clean energy projects has important implications for climate change mitigation in both non-Indigenous and Indigenous communities. As a result of a literature review, this paper defines “community energy” as activities – including initiatives with a variety of functions such as generation, retail, distribution and demand – that involve a high degree of community participation, ownership and control, where collective benefits are shared throughout the community (Hoicka and MacArthur, 2018, pp. 6). Many clean energy projects involving Indigenous participation exist in Canada with various forms of ownership and structures (Indigenous Clean Energy Social Enterprise, 2019; Hoicka and MacArthur, 2018) and it is likely, that those projects that meet the threshold of CE will make the best vehicles for reconciliation because the principles of CE and reconciliation align. This paper uses two secondary datasets by Indigenous Clean Energy Social Enterprise (2019) and Hoicka and MacArthur (2018) (the latter has been updated in the present study) to explore the Indigenous models of ownership and control of clean energy projects that exist in Canada and their potential link to reconciliation. This is believed to be a complete dataset of >1MW clean energy projects in Canada with Indigenous participation. It also parallels the models present in Indigenous communities with non-Indigenous communities. Additionally, the paper explores the aforementioned two datasets on clean energy projects involving Indigenous participation and a third secondary dataset by Wyse and Hoicka (2019), which is focused on local energy plans, along with some primary data to analyze the number and location of both projects and plans, the Indigenous groups (First Nations, Inuit and Métis) involved as well as their corresponding community types (off-grid/remote vs. grid-connected). A total of 198 active clean energy projects in Canada with Indigenous participation and 167 Local Energy Plans for Indigenous communities were identified. The majority of the Indigenous communities involved with both projects and plans were First Nations, grid-connected communities, with few Inuit and mixed Indigenous communities, and 0 Métis communities. For the projects, forms of ownership and control and corresponding structures are difficult to determine without significant additional research and analysis. The majority of the projects explored in this study are partnerships between Indigenous communities and non-Indigenous corporations, and there is a small number (6) that are fully Indigenous government-owned. Additionally, 1 energy co-operative was identified. The structures of these partnerships are largely unknown as this information was only available for 25 out of 198 projects in the datasets, but it is clear that structures can vary from majority Indigenous-ownership or 50/50 joint ventures, to minority Indigenous-ownership, for example. The inclusion or exclusion of all major Indigenous groups in Canada along with whether clean energy projects involving Indigenous communities reaches the off-grid, diesel-dependent communities that need it most also has important implications for reconciliation.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.361
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.005
Science and technology studies0.0000.000
Scholarly communication0.0000.004
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.016
GPT teacher head0.181
Teacher spread0.165 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations1
Published2019
Admission routes1
Has abstractyes

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