Someone else’s chain, someone else’s road: U.S. military strategy, China’s Belt and Road Initiative, and island agency in the Pacific
Bibliographic record
Abstract
The islands of the western Pacific have increasingly been portrayed by policymakers, military strategists, journalists, and scholars as places caught between a rising China and traditional powers such as the United States and their allies. In this article, however, we aim to challenge the geopolitical view of islands as ‘falling’ into the sphere of influence of one power or another. Specifically, we use an approach informed by assemblage theory to highlight the ways that islands in the Pacific simultaneously engage with multiple powers and their associated political, economic, and social influences. To ground our argument, we discuss two ‘great power’ schemes that aim to bring islands in the region into specific relational configurations: U.S. ‘littoral defense lines’ and China’s Belt Road Initiative. We also include a brief case study of Chinese tourism investment in Yap Island (Wa‘ab) in the Federated States of Micronesia (which is a state in ‘free association’ with the U.S.). Through these examples, we show how influence in the island Pacific is not a zero-sum game between foreign powers vying for hegemony. Instead, from an island perspective, residents and policymakers are attempting to weave together and navigate multiple foreign influences in ways that frustrate colonial and neocolonial logics of international relations.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".