Towards a conceptual framework for property level flood resilience
Bibliographic record
Abstract
Resilience is a multifaceted field of study that has been addressed by different disciplines and has been the subject of extensive research.Despite this vast body of research, there is no agreement on a single definition among researchers.Resilience in the context of flooding has become a major focus of flood risk management policy and reflected in current strategy to mitigate the effects of flooding.Furthermore, in recent times, increased attention has been given to property level resilience as part of an integrated approach to flood risk management.Despite this focus on resilience to flooding, there lacks a single definition and consequently, any effective means to quantify and measure resilience at the level of the individual property.This study aims to review and synthesize the concepts of resilience applied in different fields, in order to propose a resilience definition in the context of property level flood resilience.A framework for conceptualising flood resilience in residential property is developed which indicates the associated components and variables.The framework has the potential to be used by a range of key stakeholders in helping to understand current levels of property level resilience and in deciding what interventions might be best considered to improve resilience.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".