Cross-Network Weaponization in the Semiconductor Supply Chain
Bibliographic record
Abstract
Abstract How do states’ positions across multiple and interconnected economic networks affect their power? The Weaponized Interdependence (WI) scholarship emphasizes that states centrally located in global economic networks have access to new sources of coercion. In this paper, we look at how their positions across multiple networks interact with each other to create new opportunities and vulnerabilities. We use network analysis to map the semiconductor supply chain and show that it can be viewed as four interrelated networks: (1) design, (2) raw material, (3) manufacturing equipment, and (4) assembled chips. We then highlight how states’ centrality varies across these networks and how it shapes their respective opportunities for coercion. Looking specifically at the United States, we emphasize how its centrality in the design network enables it to weaponize chokepoints in the trade network of assembled chips. In so doing the paper makes three contributions. First, it highlights how interactions among multiple economic networks provide new opportunities for states to weaponize interdependence. Second, it contributes to recent attempts using network analysis to analyze structural power on the global stage. Last, it demonstrates how network methodology can help detect potential (ab)uses of WI and how the potential for weaponization evolves over time.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".