Understanding Violence and Conflict: Greenland as a Theory-Building Case Study
Bibliographic record
Abstract
Greenland is a land where unresolved conundrums and fast-paced emerging threats intersect. If not properly addressed, these could worsen the critical rates of suicide (one of the highest in the world and 6-7 times higher than the other Nordic countries), multigenerational trauma, and other forms of abuse. By developing and applying a renewed conceptualization of violent conflicts aimed at unravelling their roots, this study recognises that there are precise violent phenomena and conflictual dimensions that curb Greenlandic development across human security and international relations. The study confirms that the Danish 'benign' colonisation, by constituting a discriminatory relationship, provoked frustration among the Inuit, fostering the psychological push factors to self-destruction and violence against other fragile individuals, while environmental conditions and contextual phenomena limited violence at the micro-level. More broadly, the case study demonstrates that discrimination in its wider sense is the main source of violent conflicts and that the redistribution of the ownership of resources is the main way to prevent large and organised violent phenomena. In fact, Greenland currently needs a multi-agency psychosocial healing programme that addresses households and individual therapy....
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.021 | 0.013 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".