A Framework to Study the Resilience of Organizations: A Case Study of a Nuclear Emergency Plan
Bibliographic record
Abstract
Nowadays, building resilience is a key topic in many research fields such as Management, Engineering, Psychology or Ecology.The frequency increase of natural and anthropogenic disasters and the consciousness about their effects are among the reasons why resilience has gained importance and Governments are investing money in boosting the resilience of organizations, infrastructure, cities, individuals, etc.However, there is not much research on specific methodologies to design resilient organizations.A main goal of our research is to improve this aspect providing a framework to design resilient organizations.We explain how to design resilient organizations based on the Viable System Model principles.Then, we focus on an important aspect for being resilient: the communications.We use as a case study a Nuclear Emergency Plan from Spain to show the applicability of our framework.Since the communications in an organization can be modeled as a diffusion process in multiplex networks, and we did not find any suitable architecture to study them in the context of our case study, the architecture we design in this thesis is generic and allows us to model and simulate any kind of diffusion process in a dynamic multiplex network.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".