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Record W4385298545 · doi:10.1007/s00267-023-01854-5

Corporate Engagement Strategies in Northern Mining: Boliden, Sweden and Cameco, Canada

2023· article· en· W4385298545 on OpenAlexaboutno aff
Gregory Poelzer

Bibliographic record

VenueEnvironmental Management · 2023
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsnot available
FundersSvenska Forskningsrådet FormasEnergimyndighetenVINNOVA
KeywordsStewardship (theology)LicenseEnvironmental stewardshipFacilitatorIndigenousLivelihoodCorporate social responsibilityBusinessTransparency (behavior)Public relationsDeforestation (computer science)Forest managementCommunity engagementEnvironmental resource managementPolitical scienceAgricultureGeographyEconomicsForestry

Abstract

fetched live from OpenAlex

The role of corporations in societal outcomes continues to grow. Mining companies now face the expectation of not only providing economic benefits to communities, but act as a facilitator for social wellbeing and environmental stewardship. In the mining sector, this has placed renewed attention to defining corporate social responsibility and, in turn, how social license to operate is understood. These developments are particularly pertinent when mining operations affect Indigenous communities - where land use is central to livelihood. This study looks at the community engagement strategies of two mining companies in northern countries, Cameco (Canada) and Boliden (Sweden). By comparing their approaches, this paper explores the development of their practices over time and assess to what extent their corporate policy has translated into everyday practice and outcomes. The findings of demonstrate that high levels of trust are established when corporate approaches are built around transparency and collaboration - resulting in agreements that include long-term partnerships around socio-economic and environmental management.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.679
Threshold uncertainty score0.976

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 studies0.0000.000
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 head0.012
GPT teacher head0.174
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations6
Published2023
Admission routes1
Has abstractyes

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