Evaluation of state of resilience for a critical infrastructure in a context of interdependencies
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 concept of governmental resilience was introduced by the Hyogo Framework in 2005. It suggested a new approach to protecting critical infrastructures and understanding their interdependencies. In a resilience process, it becomes necessary to evaluate and measure the state of resilience; the state, in turn, will indicate the strengths and weaknesses on which the organisation can act. The methodology to evaluate the state of resilience is based on three important concepts: what to anticipate, plan, and maintain. Two intertwined elements are implied: knowledge and adaptation. Knowledge is the key for a better state of resilience and is provided by the developed methodology. Adaptation gives meaning to resilience in terms of time. In fact, an organisation must be able to adapt its internal environment to cope with the external environment which is constantly changing. In this article, we present the methodology to evaluate the state of resilience and its implication for an organisation.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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