Capturing practitioner perspectives on infrastructure resilience using Q-methodology
Bibliographic record
Abstract
Abstract In many disciplines, the resilience concept has applied to managing perturbations, challenges, or shocks in the system and designing an adaptive system. In particular, resilient infrastructure systems have been recognized as an alternative to traditional infrastructure, in which the systems are managed to be more reliable against unforeseen and unknown threats in urban areas. Perhaps owing to the malleable and multidisciplinary nature in the concept of resilience, there is no clear-cut standard that measures and characterizes infrastructure resilience nor how to implement the concept in practice for developing urban infrastructure systems. As a result, unavoidable subjective interpretation of the concept by practitioners and decision-makers occurs in the real world. We demonstrate the subjective perspectives on infrastructure resilience by asking practitioners working in governmental institutions within the metropolitan Phoenix area based on their interpretations of resilience, using Q-methodology. We asked practitioners to prioritize 19 key strategies for infrastructure resilience found in literature in three different decision contexts and recognized six discourses by analyzing the shared or discrete views of the practitioners. We conclude that, from the diverse perspectives on infrastructure resilience observed in this study, practitioners’ interpretation of resilience adds value to theoretical resilience concepts found in the literature by revealing why and how different resilience strategies are preferred and applied in practice.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".