Bibliographic record
Abstract
China’s preliminary inclination was to promote nuclear proliferation as a way to undermine the military strength of the established world powers, but it quickly reversed its course in both rhetoric and action after its October 16, 1964, nuclear detonation at Lop Nor. 1 China’s nuclear sharing poses a puzzle, because its development of its own nuclear arsenal seems to contradict its nuclear sharing policy. China’s nuclear sharing policy is liberal, whereas the development of its own nuclear arsenal is very conservative. The explanation is that China is taking a measured but considered risk by not building up sufficient force to ride out a US or Russian first strike. Instead, China is focusing its efforts and resources on economic development, and relying on inexpensive sharing to shore up allies on its periphery. China had generally colluded with the Soviet Union in restraining a North Korean nuclear arsenal, but its interest in maintaining a buffer has not led it to engage in more confrontational or rollback policies against North Korea’s nuclear arsenal. China has furthermore helped Pakistan’s and Iran’s nuclear prospects, and has apparently allowed North Korea and Myanmar to improve theirs. This sharing behavior with states on China’s periphery is prone to costly blowback, because all of these states have alternate allies (the US or Saudi Arabia for Pakistan, Russia for North Korea) and can therefore resist Chinese compellence. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".