Bibliographic record
Abstract
As China and North Korea aggressively assert their growing capabilities and destabilize the Indo-Pacific region, Japan and other like-minded nations are considering various options to confront the growing threat. Thus, Japan has recently begun to strengthen its military cooperation with the United States, the United Kingdom, Australia, New Zealand, and Canada, all of which are members of the Five Eyes (FVEY) intelligence-sharing group. Extensive intelligence cooperation, however, has continued to lag between Japan and the FVEY members.This thesis therefore examines the factors that have prevented Japan from pursuing a deeper intelligence-sharing relationship with FVEY and whether they remain as obstacles to Japan’s formal ascension to the most extensive and exclusive intelligence-sharing group as a Sixth Eye. The research suggests that three independent variables affect Japan’s intelligence-sharing relationships: 1) increased threat perception, 2) strengthened intelligence institutions, and 3) increased trust with its security partners. Further analysis of these variables reveals that they all affect intelligence sharing differently across time periods. Changes in certain independent variables may not produce noticeable increases in intelligence sharing until decades later. As such, research has shown that Japan’s lack of sufficient institutional protections regarding state secrets remains the primary obstacle to its ascension to FVEY as a sixth member.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".