Mapping the Literature on Social Responsibility and Stakeholders’ Pressures in the Mining Industry
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.029 | 0.044 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".