DNA: Diplomacy, Negotiations, and the Arctic: An analysis of the friendliest land border dispute in the world, the Hans Island conflict
Bibliographic record
Abstract
This master’s thesis aims to analyze the Hans Island conflict through the lens of constructivist theory in international relations with the help of a case study as a method. The thesis explores the reasons why this conflict took over 50 years to be settled, how it was solved, and what was the outcome. The first part of the thesis includes a general introduction to the topic as well as introductions to negotiations, conflicts, and the Arctic region. The timeline of the events during the conflict as well as the analysis of the matter is followed up in the second part of the thesis. The thesis concludes with the findings chapter, where the reader is presented with answers to the research questions. \n\tThe main conclusion of the theory is that the Hans Island conflict is a very rare example of how land border conflicts are solved. There was no threat throughout the conflict, no military presence. The three states, Canada, Denmark, and Greenland have shown a willingness to work together in a peaceful manner to solve the issues. The reasoning for a peaceful dispute could be the fact that the three states are allies within the larger organization, which means that the three states have similar views and beliefs in the international environment. The Agreement was officially signed on the 14th of July 2022, which marked a very significant moment for Indigenous communities. Throughout the process of solving the conflict, Indigenous communities were consulted and involved. This is seen as a first step in the actual recognition of Indigenous territories. This thesis explores the conflict using the collection of available data from all three states and analyzing the deeper meaning of the state's views on the conflict resolution process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".