Mineral diplomacy in Greenland: learning from US-European history of engagement at the Ivittuut cryolite mine
Bibliographic record
Abstract
Growing extractive and geopolitical interest in Greenland is leading to a confluence of threats and opportunities for communities in determining their future economic development pathways. This paper investigates the diplomatic history of the Ivittuut cryolite mine in Southwest Greenland as part of a larger participatory mapping project focused on interviewing stakeholders in the Arsuk fjord on future development pathways in the Ivittuut region. Ivittuut was a key strategic outpost supporting U.S and European cooperation during World War II. In particular, we define the political process of mineral diplomacy and how cryolite was at the heart of US-European diplomacy in Greenland and how minerals could potentially again be a source of cooperation rather than conflict. Greenland can be a frontline for mineral diplomacy, bridging interests between Europe and the US but key to moving forward with such a process requires critical reflection of who was excluded and included in previous mining engagements. Mineral diplomacy today often reflects colonial extractive pressures and military strategic interests, but we argue that political geology frameworks must be included to ensure that mineral diplomacy processes foster long-term sustainable mineral investment while ensuring local communities and Greenlandic values are protected and included in throughout the process.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 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".