Geopolitical rivalry over strategically important industries: understanding the effects on global supply chain design
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it