Globalisation and Network Resilience: A Special Issue Introduction
Bibliographic record
Abstract
ABSTRACT This special issue examines globalisation and resilience, variously conceived, from a network perspective. In an era that moved from hyperglobalisation to disruption—pandemics, geopolitical tensions, climate risks—we argue that a key orienting question should be how globalisation is being reconfigured across multiplex economic, social and industrial networks. With this special issue, we hope to motivate new bodies of literature deploying social network analysis to diagnose and analyse the resilience of global economic networks to exogenous shocks. Where are such shocks likely to occur? Do they get contained in network subgraphs? Or are they absorbed more equally throughout the network? In any given network, which actors and ties, or types of actors and ties, underpin systemic robustness? The four papers in the issue span a bibliometric synthesis of ‘network resilience’ across domains; an industry‐level measure of supply‐chain disruption linking logistics reliability to US output; a country‐level study connecting embeddedness in the global FDI network to democratic resilience in less‐developed countries; and a firm‐level reconstruction of the EV corporate ownership network. We conclude by highlighting the substantive contributions of these papers, by calling for conceptual clarity on network resilience, and by suggesting a number of fruitful directions for future research.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.030 | 0.005 |
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".