Exploring the Significance of Earning a Social License to Operate in an Urban Setting
Bibliographic record
Abstract
Mining companies are increasingly approaching the social aspect of sustainable development within the rural communities that neighbour their projects and operations. Rural community engagement requires significant effort and resources, and it can be extremely challenging for mining companies to earn and sustain social capital. The focus on rural, proximal community engagement is not to be understated and has led to significant benefits in many communities. However, opposition to mining projects is often exhibited in urban environments, where there may be potential for mining companies to gain social capital with relative ease. Cities tend to have existing frameworks for community engagement and public activation, such as annual parades and festivals, that make it easy for a mining company to provide financial support or value-in-kind. Local organizations and community groups can achieve the same amount of engagement in an urban environment with significantly less effort required from the mining company. By applying simple marketing principles to community engagement strategies, corporations can increase awareness for their business and encourage city residents to think critically about the origin of resources. Unlike many corporations, mining companies are not marketing or selling products to individual consumers. From this arises the opportunity for a company to use marketing to promote other positive initiatives and, as such, connect their brand with positive messaging thus earning social capital. This may lead to a wide variety of secondary impacts including benefits to recruitment efforts, increased political support, and positive media coverage. This paper explores these matters with special reference to Vancouver, BC, Canada and the mining companies Teck and Goldcorp.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".