(Re)emphasizing Urban Infrastructure Resilience via Scoping Review and Content Analysis
Bibliographic record
Abstract
Although the importance of urban infrastructure resilience can be inferred, its terminology remains convoluted within the literature due to a lack of systematic review from a sustainable development planning perspective. This review paper was designed to elucidate connected research themes, scientific popularity, and conceptual boundaries of the term infrastructure resilience in an urban context. Three guiding research questions were asked: What does urban infrastructure resilience really mean? What are the most common research topics connected to urban infrastructure resilience? How can humanity further improve urban infrastructure resilience from a sustainable development planning perspective? To answer these research questions, a two-step literature analysis was adopted consisting of: (i) a scoping review to select relevant publications based on a specific search query; and (ii) a content analysis to reduce and synthesize the scoping review findings further based on the three most applicable publishing outlets. The scoping review reduced articles to 535, while content analysis further condensed it to 84 across three key journals. With North America and Europe leading, the findings corroborated that eight connected subject areas establish the conceptual boundaries of urban infrastructure resilience. The eight related research topics in decreasing abundance were: (1) climate change, (2) floods, (3) disasters, (4) environmental policy, (5) ecosystems, (6) risk assessment, (7) emergency preparedness, and (8) adaptation. In conclusion, these research topics should be pursued when creating urban infrastructure resilience strategies for moving towards sustainability.
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.
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 | 0.200 | 0.421 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.041 | 0.028 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.018 | 0.020 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".