Are Economic Sanctions against North Korea Effective? Assessing Nighttime Light in 25 Major Cities
Bibliographic record
Abstract
This study analyzes the effects of the economic sanctions against North Korea since 2016 on the economic well-being of North Korean cities. As a proxy for economic well-being, we use nighttime light (NTL), which we estimate from 1992 to 2019 through an inter-calibration process for DMSP/OLS and SNPP/VIIRS. We found that NTL in North Korea was getting brighter up until 2009, but that the growth rate of total NTL in 25 major North Korean cities began to decrease from 2016. The decline in the NTL growth rate of Pyongyang, the capital city, as well as in cities bordering China and in self-regenerating cities, was relatively slight. By contrast, the declines in the NTL growth rate of coal-mining cities and inland cities without sufficient production bases were greater than those in other cities, and some cities experienced negative growth in 2019. Cities in regions relying on coal mining have traditionally accounted for a large portion of North Korea’s exports, and since these cities have been heavily affected by sanctions, coal mining could become a vulnerable sector, which would threaten North Korea’s economic well-being.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".