Food Security or Food Sovereignty? Agricultural Technology Reforms after the Famine in North Korea
Bibliographic record
Abstract
How did North Koreans reform their agricultural technology after the massive famine in the 1990s? While the existing literature focuses its analysis on the nexus between the state and market to assess the possibility of a transition economy, we instead examine agricultural methods and technologies employed in farmlands to evaluate the nature of technological reforms. After identifying technology reforms on the basis of primary sources published in the DPRK such as yearbooks, academic journals, and newspaper articles, as well as other materials published in South Korea, Japan, and the United States and by international organizations, we classify them into two kinds of initiatives: modernization measures that sought to address the earlier failure to modernize agricultural technologies, and ecology-friendly farming practices designed to reduce or reverse the negative externalities of industrial agriculture such as overdependence on chemical fertilizers or erosion of soil fertility. While the two are commonly seen as incompatible by scholars of agriculture, we conclude that North Koreans synthesized the two to transform their decaying industrial agriculture into a more modernized and ecology-friendly sector. They have, through these reforms, maintained food sovereignty as their pillar of agriculture but complemented it with food security on a national scale as a way to maximize their agriculture outputs. Most of these initiatives seem to continue to date although it remains to be seen if they have actually succeeded in increasing overall agricultural productivity and sustainability.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".