Building resilience in virtual digital response networks: a case study
Bibliographic record
Abstract
The evolution of technology is creating a more complex, connected society of interdependent networks and processes. As connectivity increases, so does the concentration of value. This increases the consequence of failure and the scale, scope and complexity of potential risks causing these systems to fail. These systems are made resilient by making the physical and virtual networks resilient in isolation and intersection. Yet existing resilience practices fail to address the complexities of virtual networks and their dynamics with the physical environment, specifically the requirements of physical infrastructure networks to enable resilient virtual networks and vice versa. This paper aims to address this gap through a simulated case study of resilience development within and between a physical network and a virtual online network. The networked operational resilience framework is applied to an emergency services network partnered with a digital response network (DRN). DRNs are virtual networks of crowdsourced volunteers that respond to the virtual layer of crisis. Building situational awareness to aid decision-making, they have become an essential tool of crisis response. Findings address the context of virtual online networks in isolation and partnership, enabling infrastructure requirements, the risk environment and resilience capability and development.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".