The road to societal trust: implementation of Towards Sustainable Mining in Finland and Spain
Bibliographic record
Abstract
Abstract In government, industry and academia, there is a convergence of three trends: (1) the belief that responsible exploration and mining should increase across Europe, (2) industry should follow and ‘Europeanise’ international good practices and (3) a social licence to operate exists not only between a community and a company but also between society and industry. There are two examples in Europe where these trends are converging—Finland and Spain have both adopted the Canadian Toward Sustainable Mining (TSM) program, but the method of implementation is very different. As a result of Talvivaara, Finland took a network governance approach incorporating trust-building measures from the beginning by bringing diverse stakeholders together to create the Finnish Network for Sustainable Mining. Spain chose to integrate the TSM into their national standards, a more traditional and hierarchical approach but one that also relies on a trustworthy entity with clear longevity. Although implementation is in the early stages in both countries, and therefore this paper provides insights only on preliminary outcomes, results indicate that the network approach may not be better at achieving societal SLO suggesting that other factors such as narrative, dialoguing directly with society, implementing trust-building measures in a timely fashion and proven longevity may have more influence than early trust-building measures between network participants.
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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 | 0.019 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".