Geopolitical rivalry over strategically important industries: understanding the effects on global supply chain design
Bibliographic record
Abstract
This article seeks answers to the question: how does geopolitical rivalry over strategically important industries impact the design of global supply chains? To answer this question, we examine how high technology firms responded to the United States (US) and Chinese government policies related to protecting national technological competitiveness. The study pays particular attention to how high technology firms moved sources of supply and production sites in response to protectionist government policies, and the new supply chain designs that emerged. The research question is examined through the lens of Resource Dependence, Resource Orchestration and Institutional Theory. A comparative case study design is used to contrast how high technology firms in the semiconductor and rare earth industries have responded to a technological rivalry between the US and China. Twenty-three interviews were conducted with senior managers and supply chain executives working at 13 semiconductors and eight rare earth metals companies, all of which had operations in the USA, China, or both. The comparative case analysis provides insights into the different actions that companies take to reconfigure their supply chains in response to geopolitical tensions. The study’s findings inform Geopolitical Resource Orchestration and Proactive Disruption Risk Mitigation frameworks, which outline potential mitigation measures that companies and policymakers can take to alleviate the impact of geopolitical tensions on global supply chains.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".