Balance of the Trade-off Between Accountability and National Security: A Case Study of Yonaguni Island, Okinawa, Japan
Bibliographic record
Abstract
This paper examines how the dysfunction of accountability mechanisms, caused by information asymmetry, hinders consensus-building among actors in the context of establishing NIMBYs with high levels of confidentiality and technical expertise. As a case study, this paper analyzes developments on Yonaguni Island, one of Japan’s border islands, regarding the invitation of the Japan Self-Defense Forces – a case that may be considered a deviation from conventional NIMBY issues. Initially, the local municipality and some residents of Yonaguni Island jointly invited the JSDF, and consensus was reached through a local referendum; however, after the establishment of the Camp, even some former proponents began to express doubts. This paper categorizes the relevant actors as those who impose accountability (holders) and those upon whom accountability is imposed (holdees), and analyzes the extent to which the duty of explanation has been fulfilled. The analysis shows that when a holdee responds to a holder’s request for explanation by providing information and fulfilling their explanatory duty, consensus-building is fostered; conversely, when sufficient information is not provided by the holdee to the holder, the resulting expansion of information asymmetry poses a risk of consensus breakdown.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".