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Record W4285793138 · doi:10.1007/s00267-022-01681-0

Community as Governor: Exploring the role of Community between Industry and Government in SLO

2022· article· en· W4285793138 on OpenAlexaffabout
Gregory Poelzer, Rosette Frimpong, Greg Poelzer, Bram Noble

Bibliographic record

VenueEnvironmental Management · 2022
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsUniversity of Saskatchewan
FundersSvenska Forskningsrådet FormasEnergimyndighetenVINNOVA
KeywordsIndigenousCorporate governanceGovernment (linguistics)GovernorNatural resourceEnvironmental governanceResource (disambiguation)Environmental resource managementForest managementCommunity-based managementNatural resource managementBusinessEnvironmental planningPublic relationsPolitical scienceEcologyEconomicsGeographyFinanceEngineering

Abstract

fetched live from OpenAlex

For many natural resource projects, the impact on Indigenous communities is a primary concern. Therefore, governance arrangements that account for the interests of companies, communities, and government are critical for the project's success. This paper looked at two successful mining projects in northern Canada, McArthur River and Diavik, to examine the governance arrangement that led to mutually beneficial outcomes. Through an analysis of interviews and documents, we assessed both governing institutions and interactions to understand how the respective companies and communities established a high level of trust. In both cases, government took a less prominent role in the management of resources, allowing the Indigenous communities to hold a stronger role in the governance of the resources. Both Indigenous communities, therefore, built partnerships with the company around socio-economic benefits along with environmental monitoring - redefining 'community' in governance arrangements.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.267
Threshold uncertainty score0.530

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0180.023
Scholarly communication0.0080.007
Open science0.0010.011
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.015
GPT teacher head0.177
Teacher spread0.162 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2022
Admission routes2
Has abstractyes

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