Dynamic criticality for infrastructure prioritization in complex environments
Bibliographic record
Abstract
Abstract As infrastructure confront rapidly changing environments, there is an immediate need to provide the flexibility to pivot resources and how infrastructures are prioritized. Yet infrastructures are often categorized based on static criticality framings. We describe dynamic criticality as the flexibility to reprioritize infrastructure resources during disturbances. We find that the most important prerequisite for dynamic criticality is organizational adaptive capacity characterized by flexible goals, structures, sensemaking, and strategies. Dynamic capabilities are increasingly important in the Anthropocene, where accelerating conditions, uncertainty, and growing complexity are challenging infrastructures. We review sectors that deployed dynamic management approaches amidst changing disturbances: leadership and organizational change, defense, medicine, manufacturing, and disaster response. We use an inductive thematic analysis to identify key themes and competencies and analyze capabilities that describe dynamic criticality. These competencies drive adaptive capacity and open up the flexibility to pivot what is deemed critical, depending on the particulars of the hazard. We map these competencies to infrastructure systems and describe how infrastructure organizations may build adaptive capacity toward flexible priorities.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".