Resilient cities critical infrastructure interdependence: a meta-research
Bibliographic record
Abstract
Given the unforeseen events that take continue to place worldwide, cities are experiencing rapid transformations. To maintain their basic functions, cities have to be resilient– possess the ability to bounce back to their original state following extreme events. Unfortunately, the behavior of cities is complex because of the interdependence among their comprising infrastructure systems. The current work presents a critical review of research work pertaining to resilience of cities’ critical infrastructure systems. To conduct such review, meta-research is employed through text analytics, in the form of topic modelling, to quantitatively uncover related latent topics in pertinent literature. Subsequently, the identified topics are qualitatively analyzed in terms of established definitions and metrics for resilience as well as adopted simulation approaches for infrastructure systems interdependence. Accordingly, nine common topics and five major research gaps are identified. This meta-research study is a steppingstone towards better understanding of infrastructure systems interdependence simulation and their resilience quantification approaches.
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.019 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".