Building resilience in virtual digital response networks: a case study
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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