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Record W4285983773 · doi:10.1016/j.erss.2022.102711

When the environment is destroyed, you're destroyed: Achieving Indigenous led pipeline justice

2022· article· en· W4285983773 on OpenAlexafffundabout
Margot Hurlbert, Ranjan Datta

Bibliographic record

VenueEnergy Research & Social Science · 2022
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsMount Royal University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsIndigenousSovereigntyTreatyPolitical scienceEconomic JusticeEnvironmental justiceLawOpposition (politics)Environmental ethicsEconomic growthSociologyPoliticsEcologyEconomics

Abstract

fetched live from OpenAlex

Indigenous communities in Canada are at high risk of suffering damage from, and disproportionately impacted by pipeline spills. However, historically their opposition to pipeline development has largely been unsuccessful. Based on socio-legal applied research including a legal analysis, as well as twenty interviews with Indigenous knowledge keepers in Alberta and Saskatchewan, this article explores Indigenous perspectives surrounding oil and gas pipelines, Indigenous pipeline justice, and specifically what an Indigenous led pipeline relation would look like. These research findings offer insight into future Indigenous energy justice. Two foundational pillars of Indigenous pipeline justice are: 1. Sovereignty/Treaty; and 2. Relations with Mother Earth and each other. Both are inextricably intertwined with conceptions of time and trust. Sacred treaty promises included sharing the land and its resources for as long as the sun shines and the waters flow. Mother Earth cannot be valued with money. Results highlight the opportunity for achieving Indigenous pipeline justice by implementing the United Nations Declaration of the Rights of Indigenous People and advancing recognition justice.

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)0.004
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.835
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0050.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.034
GPT teacher head0.286
Teacher spread0.252 · 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
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
Published2022
Admission routes3
Has abstractyes

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