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Record W4385408127 · doi:10.1016/j.heliyon.2023.e18837

Just transition in the northwest territories: Insights and values from indigenous and non-indigenous northerners

2023· article· en· W4385408127 on OpenAlexafffundabout
Candice Anita Amber, Sandeep Agrawal, Celine Zoe

Bibliographic record

VenueHeliyon · 2023
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsAlberta Environment and Protected AreasUniversity of Alberta
FundersCanada First Research Excellence Fund
KeywordsIndigenousOutreachAutonomyEnergy transitionClimate changePolitical scienceEconomic growthEnvironmental resource managementGeographyEconomicsEcology

Abstract

fetched live from OpenAlex

Just transition means that society shares both tangible and intangible costs and benefits of transitioning to a low-carbon economy in a socially just way. Across Canada, Indigenous peoples are shouldering a disproportionate social and economic burden on non-renewable sources as well as transitioning to renewable sources of energy due to high costs, lack of appropriate technology to store excess power, and remoteness of the region. This study aims to promote the significance of Northern energy transition through Indigenous perspectives (technological-social) in advancing a low carbon future as an act of truth and reconciliation (2015) in the Northwest Territories (NWT). In the NWT successful progress for climate change issues have been made pre-Covid with a plurality of perspectives but there is room for an improved post-Covid process that requires an emphasis on the inclusion of Indigenous perspectives (technological-social) as equal to non-Indigenous perspectives (technological). Results identify three themes, which are Indigenous land ethos (Mother Earth relationality), community energy autonomy (informed leadership), and capacity training (humanizing outreach) as key drivers to future just transition in the Northwest Territories.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.556
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.027
GPT teacher head0.322
Teacher spread0.296 · 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

Citations7
Published2023
Admission routes3
Has abstractyes

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