From Resilience to Flexibility: Urban Scenario to Reduce Hazard
Bibliographic record
Abstract
Population estimates and projections demonstrate that, in the next future, human beings, buildings, economy and society will be exposed to multiple effects (direct and indirect) of phenomena capable of producing temporary and permanent damages on natural environment.The city, as complex system, in case of slow changes, is able to modify its main components and relations (physical and social).During and after unexpected disasters (i.e.floods, earthquakes, hurricanes, terrorist attacks, etc.), its ability to absorb external impacts, to transform and to adapt itself in order to find a new equilibrium status, is particularly stressed and involves the concept of resilience.To be resilient, town should change unceasingly to adapt to the continuous modification of citizens and environment demands and to find a new balance in different dimensions (flexible city).The paper aims to give some strategically recommendations that will contribute to reduce disaster risk at urban level.Investigating the future of urbanization and of urban theory, the authors try to rethink the city as a dynamic space that better responds to evolving circumstances and contemporary global challenges: concepts of resilient and flexible cities are involved.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".