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Record W4412372606 · doi:10.1111/joms.13264

Clash of the Titans: Conflict Intensity, Community Dependence on Environmental Resources, and Stakeholder Multiplicity

2025· article· en· W4412372606 on OpenAlexfundno aff
Chang Hoon Oh, Jiyoung Shin

Bibliographic record

VenueJournal of Management Studies · 2025
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsStakeholderMultiplicity (mathematics)Intensity (physics)BusinessEnvironmental resource managementNatural resource economicsPolitical scienceEconomicsPublic relationsPhysicsMathematicsOptics

Abstract

fetched live from OpenAlex

Abstract Extending the stakeholder multiplicity perspective and resource dependence theory, this study investigates how the dependency of both a company and a community on environmental resources for livelihoods influences the conflict intensity between them. We theorize that companies face difficulties in managing key stakeholders when these stakeholders heavily rely on environmental resources for their livelihoods. We also argue that their conflicted relationship depends on surrounding stakeholders' involvements. This study highlights that stakeholder multiplicity shapes the dynamics in stakeholder salience (competing or cooperating), which in turn affects companies' approach toward them. The study analyses 318 global mining conflicts spanning from 2002 to 2013 using a hand‐collected, multi‐source dataset. Empirical findings reveal that local communities' dependence on environmental resources increases the intensity of conflicts between mining companies and communities. The positive effect of communities' resource dependence on conflict intensity is weakened by the involvement of environment‐oriented (vis‐a‐vis growth‐oriented) government actors. In contrast, the lack of support from broader society (societal inequality) strengthens this effect.

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)0.001
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 score0.501
Threshold uncertainty score0.320

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.045
GPT teacher head0.246
Teacher spread0.201 · 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

Citations4
Published2025
Admission routes1
Has abstractyes

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