North Korean Perceptions of Self and Others: Implications for Policy Choices
Bibliographic record
Abstract
T he Korean peninsula is known as the most heavily armed area in the world in terms of both manpower and weaponry. One need only travel to the DMZ to see the massive forces confronting one another. What is not as obvious to the casual observer is the fact that the security of this region is most unpredictable and uncertain due to the pervasive mistrust that exists between the two systems. This remains true despite the inter-Korea summit meeting in June 2000 that led to a series of reconciliatory gestures from both sides, such as uniting separated families, exchanging performing artists, and entering the Sydney Olympic stadium under the same flag. North Korea is still vilified as the singular actor that is irrational, unpredictable, and lacking civility. Such criticisms are made of North Korea with little knowledge about the thought processes, policy motivations, and behavioural traits of this alienated system. The purpose of this paper is to shed some light on the policy goals, strategies, and tactics that North Korea has employed in the post-cold war years. In so doing, it is hoped that we can empathize with North Korean decision-makers to enable us to explain better Pyongyang's policy behaviour. The perspective employed in this essay may be called the phenomenological approach in the sense that North Korean perceptions constitute the reality and that these perceptions must be articulated from the standpoint of the perceiver.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".