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Record W4247942769 · doi:10.24124/2017/58919

Building a network of clean energy systems: A case study of the T'Sou-ke First Nation solar project

2017· dissertation· en· W4247942769 on OpenAlexaboutno aff
Ananya Bhattacharya

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicSocial Acceptance of Renewable Energy
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)Solar energyAutonomyGreenhouse gasEngineeringPolitical scienceEnvironmental resource managementBusinessEnvironmental planningGeographyEnvironmental science

Abstract

fetched live from OpenAlex

During the last ten years, several Aboriginal communities in British Columbia (BC) have built various forms of clean energy systems with some form of government and private support. There is, however, little comprehensive scholarly analysis of these projects, ones that evaluate their ability to meet the interests of a community and the factors determining their success. This research undertakes a case study analysis of a solar energy project in BC installed in 2009 by the T’Sou-ke First Nation, near the southern tip of Vancouver Island. In this research, I examine the evolution of the solar project, assess the impacts of the project on the community, and evaluate the replicability of the project in other communities. The results of my case study are as follows: First, the solar project evolved as a result of comprehensive Community Planning by the community. Second, the solar project had four main impacts on the community namely, limited energy autonomy, a small net reduction in greenhouse gas emissions, short to medium term employment benefits, and local community and other economic benefits. Third, the main component of the project, that is, the grid-tied PV systems is still difficult to replicate in other communities without any form of government and private support. I also identify the lessons from this project that will be helpful for other communities interested in solar and other clean energy projects.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.568
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.041
GPT teacher head0.332
Teacher spread0.291 · 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 designQualitative
Domainnot available
GenreEmpirical

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

Citations2
Published2017
Admission routes1
Has abstractyes

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