Innovation Networks and Cross-Border Knowledge Flows Under Geopolitical Fragmentation: Governance Fit, Modularity, and Interoperability
Bibliographic record
Abstract
Cross-border innovation is increasingly shaped by geopolitical rivalry, export controls, sanctions compliance, data localization, and selective decoupling. These forces do not simply reduce global connectivity; they reconfigure how innovation networks form, govern exchange, and learn across borders.Methods: This review integrates scholarship on interorganizational networks, global value chains, innovation systems, and economic geography. A multilevel framework links policy shocks to tie-level frictions, network restructuring, and firm learning outcomes.Results: Fragmentation changes cross-border knowledge flows through three mechanisms: (1) compliance friction that lowers tie bandwidth and slows joint problem-solving; (2) constraints on talent mobility and data movement that weaken tacit knowledge transfer; and (3) standards divergence that reduces interoperability and increases coordination costs. Firms respond by rewiring partner portfolios, modularizing R&D, using clean-room collaboration for regulated data and IP, and regionalizing innovation activity with redundancy.Conclusions: Post-fragmentation performance depends less on network size and more on governance fit. Firms that match knowledge type with appropriate governance (modularity, controlled interfaces, selective deep ties, and auditable collaboration) are better positioned to protect critical knowledge while sustaining exploratory learning.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".