The Canary in the Cage: Community Voices and Social License to Operate in Central Eastern Europe
Bibliographic record
Abstract
This paper is the product of SRK Exploration Services UK through the EU funded Horizon 2020 INFACT project and research undertaken as part of the doctoral study by an associate consultant. It marries European wide research through INFACT with doctoral research into the mining sector and communities in Serbia, to illustrate general and country specific issues around social license to operate. The paper illustrates how gaps are created in the extractive industries conceptualization of host communities and how that precipitates social licence to operate failures. It challenges the extractive industries use of the stakeholder concept and questions how equipped the sector is to engage and assess community-based business risk. Solutions to social licence failure in the extraction industry involve engaging with the conflict and enabling dissenting voices at an early stage of project development, rather than quelling them. Resolution to social licence issues is more likely if local communities are supported to retain control of and articulate potential conflict, rather than the conflict being captured and utilized by external actors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".