Los intereses de Estados Unidos y los dilemas de Groenlandia
Bibliographic record
Abstract
In December 2024, Donald Trump, without having yet taken office as President of the United States, expressed his interest in buying Greenland. This statement has generated a strong reaction on the other side of the Atlantic Ocean and has been clearly rejected by the Danish Prime Minister and other European leaders. Greenland is an island, the largest in the world, located in the Arctic, which is mostly populated by indigenous Inuit and belongs to the Kingdom of Denmark. Greenland also has numerous resources, including rare earth elements, which are highly coveted by China, the United States and the European Union, crucial for the manufacture of batteries, wind turbines and solar panels, and indispensable in the energy transition. Furthermore, Greenland is a crucial point in the navigation of two of the three Arctic routes —the Northwest Passage and the still impracticable Transpolar Route—. Added to this is its privileged strategic location in an Arctic that is deeply tense following the Russian Federation’s aggression against Ukraine. It is in this context that Washington’s initiative arises. This article analyses the different aspects of the proposal and the reasons behind it, but also the dilemmas faced by the Greenlandic population, which longs for independence from Denmark in an extraordinarily complex context.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".