Social License Narrative in the Mining Sector from a Cultural Perspective in Europe
Bibliographic record
Abstract
The concept of social license to operate (SLO), rooted in corporate social responsibility (CSR), emphasizes the importance of companies engaging with stakeholders and addressing social and environmental concerns (Verrier et al., 2022). To gain and maintain a social license, organizations must effectively communicate, address concerns, and demonstrate commitment to sustainability (Prno & Slocombe, 2012). Losing a social license can result in opposition, protests, and reputational damage, while maintaining one can increase trust and competitiveness (Boutilier et al., 2012). Moreover, cultural perspectives wield significant influence in the mining sector, shaping the way local communities perceive and interact with operations. For instance, in certain indigenous communities in Canada and Europe, mining is viewed as a threat to their way of life and the environment, sparking conflicts and resistance against mining companies (Gobby et al., 2022). Conversely, in some Australian regions, mining is regarded as a catalyst for economic growth and job opportunities, garnering support for mining projects (Eklund, 2015). Grasping and respecting such cultural perspectives is not only crucial but also enlightening for sustainable mining practices (Moomen et al., 2020). By conducting an extensive literature review and in-depth analysis of case studies, this study seeks to uncover how companies in the European mining sector can obtain and sustain their social license narrative to address cultural variations, with a specific focus on comparisons with Canada and Australia. These countries were chosen due to their prominent mining industries and diverse cultural perspectives, shedding light on potential strategies for navigating cultural differences in the European context. By conducting a thorough narrative analysis, the study aims to shed light on the impact of cultural components on the social licence for mining activities and how companies can leverage cultural diversity to strengthen their ties with local communities. Specifically, this study will examine the cultural factors that influence how communities perceive and respond to mining projects, the role of historical and societal values in shaping attitudes towards mining, and the importance of engaging with cultural norms and traditions in securing SLO. Finally, we discuss mining companies' challenges and opportunities in navigating European cultural dynamics and suggest strategies for building stronger relationships with local communities.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".