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Record W2904856057 · doi:10.14430/arctic4748

Beyond the Berger Inquiry: Can Extractive Resource Development Help the Sustainability of Canada’s Arctic Communities?

2018· article· en· W2904856057 on OpenAlexaffvenueabout
Chris Southcott, Frances Abele, David Natcher, Brenda Parlee

Bibliographic record

VenueARCTIC · 2018
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsUniversity of AlbertaUniversity of SaskatchewanCarleton UniversityLakehead University
Fundersnot available
KeywordsSustainabilityResource (disambiguation)ArcticSustainable developmentEnvironmental resource managementBusinessEnvironmental planningThe arcticPolitical scienceGeographyEconomicsEcology

Abstract

fetched live from OpenAlex

The four decades since the Berger Inquiry have produced a large body of research demonstrating the positive and negative impacts of resource development on northern communities. However, little independent research has aimed to yield an understanding of how best to manage the impacts of resource development and to harness its benefits in ways that can promote long-term sustainable development. This question was the impetus for the Resources and Sustainable Development in the Arctic (ReSDA) research project funded by the Social Sciences and Humanities Research Council of Canada in 2011. Representing a network of researchers, community members and organizations, ReSDA researchers conducted a series of analyses that focused on what was needed to ensure that northern communities received more benefits from resource development and potential negative impacts were mitigated. Overall, the analyses highlight the serious gaps that remain in our ability to ensure that resource development projects improve the sustainability of Arctic communities. These gaps include a proper understanding of cumulative impacts, the ability of communities to adequately participate in new regulatory processes, the non-economic aspects of well-being, the effects of impact and benefit agreements and new financial benefits, and new mitigation activities.

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)0.002
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 score0.239
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0100.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.038
GPT teacher head0.340
Teacher spread0.302 · 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

Citations22
Published2018
Admission routes3
Has abstractyes

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