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Record W4297235838 · doi:10.3390/jrfm15100425

Mapping the Literature on Social Responsibility and Stakeholders’ Pressures in the Mining Industry

2022· article· en· W4297235838 on OpenAlexvenueno aff
Margarida Rodrigues, Maria do Céu Gaspar Alves, Rui Silva, Cidália Oliveira

Bibliographic record

VenueJournal of risk and financial management · 2022
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsnot available
FundersFundação para a Ciência e a Tecnologia
KeywordsScopusCorporate social responsibilitySustainabilityStakeholder engagementMining industryStakeholderKnowledge managementSocial responsibilityLegitimacyBusinessPublic relationsPolitical scienceComputer scienceEngineering

Abstract

fetched live from OpenAlex

Mining activities can be good for the local economy, but they can also have a negative impact, which has created increasing pressure from stakeholders. A constructive and positive engagement between a company and its stakeholders is important for sustainability issues and can provide a shared understanding of sustainable development. This review aims to examine the growth trajectory, the most influential documents, and the conceptual framework of the literature on stakeholder engagement and corporate social responsibility (CSR) in the mining industry. Moreover, tries to answer the following research questions: What research streams have been followed? Which theories and research paradigms have been used? A bibliometric analysis was performed using 149 documents extracted from the Web of Science and Scopus databases. The documents obtained were analysed using Bibliometrix software. The results suggest that the most studied constructs within the mining industry are related to sustainability issues, management and legitimacy concerns, and the importance of stakeholders, particularly local communities, and the social impacts that mining generates. The study contributes to the literature by reviewing prominent cited references and documents that cited them, the authors provide the landscapes and research gaps of major research lines for further development.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.664
Threshold uncertainty score0.367

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 integrity0.0000.001
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 head0.030
GPT teacher head0.218
Teacher spread0.188 · 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 designOther design
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

Citations13
Published2022
Admission routes1
Has abstractyes

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