Resilience of Social-Infrastructural Systems: Functional Interdependencies Analysis
Bibliographic record
Abstract
Critical infrastructures serve human activities and play an essential role in societies. Infrastructural systems are not isolated but are interdependent with regard to social systems, including those of public health and economic and sustainable development. In recent years, both social and infrastructural systems have frequently been in dysfunction due to increasing natural or human-made disasters and due to the internal and external dependencies between system components. The interconnectedness between social-infrastructural systems (socio-economic systems and technical-infrastructural systems), implies that the damage to one single system can extend beyond its scope. For that reason, cascading dysfunction can occur and increase system vulnerability. This article aims to study the functional interdependencies between social-infrastructural systems and to propose a methodology to analyse and improve the resilience of these systems. Combining Actor Network Theory and the Functional Models approach, the social-infrastructural Interdependence Resilience (SIIR) framework was proposed. To assess the applicability of the approach, the framework was applied to study the interdependence of a social-infrastructural system in the Nantes Metropolis. The studied system was composed of the local Highway Infrastructure (an infrastructural system) and the Emergency Medical Service (a social system). The results (1) show the feasibility of SIIR for investigating the interdependencies of two urban systems, and (2) provide a guideline for decision-makers to improve the functional interdependencies of urban systems.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".