MétaCan
Menu
Back to cohort
Record W2898497412 · doi:10.3390/su10113871

Explaining the Diffusion of Renewable Electricity Technologies in Canadian Remote Indigenous Communities through the Technological Innovation System Approach

2018· article· en· W2898497412 on OpenAlexafffundabout
Konstantinos Karanasios, Paul Parker

Bibliographic record

VenueSustainability · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovative Approaches in Technology and Social Development
Canadian institutionsUniversity of Waterloo
FundersNatural Resources CanadaEnergy Council of CanadaUniversity of Waterloo
KeywordsIndigenousSoftware deploymentContext (archaeology)Renewable energyElectricityBusinessInnovation diffusionTransformational leadershipEnvironmental economicsIntervention (counseling)Natural resource economicsEnvironmental resource managementIndustrial organizationEconomic growthEconomicsMarketingPolitical scienceEngineeringGeographyPublic relationsEcology

Abstract

fetched live from OpenAlex

This paper applies the Technological Innovation System (TIS) approach for the first time in the context of remote indigenous communities in Northwest Territories (NWT) and Ontario, Canada, to explain the diffusion of Renewable Energy Technologies (RETs). These communities need reliable and sustainable electricity to address social, environmental and economic development issues. The study examines the diffusion of RETs during the 2000–2016 period, identifies the systemic and transformational failures responsible for the functional performance of the TISs, and generates insights about factors that have the potential to sustain the development of RET projects. Findings suggest that the TIS-proposed causal mechanisms were present and performed as expected. Since the accumulation of TIS functions influences the rate of deployment of renewable technologies, policy intervention to improve local learning and networking could lead to accelerated diffusion of RETs to the benefit of remote communities and other stakeholders.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.522
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.005
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.023
GPT teacher head0.247
Teacher spread0.224 · 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 designTheoretical or conceptual
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

Citations15
Published2018
Admission routes3
Has abstractyes

Explore more

Same venueSustainabilitySame topicInnovative Approaches in Technology and Social DevelopmentFrench-language works237,207