Bibliographic record
Abstract
Purpose Disaster risk and vulnerability assessment depends on various factors such as appropriate theoretical concepts and quality and adequacy of information gathered. Accounting for people's perception and partnering with them in the process leads to deeper understanding of community vulnerability, which in turn provides better assessment of disaster risk. The purpose of this paper is to offer an integrated approach for risk and vulnerability assessment that includes theoretical concept, quantitative risk assessment method, and a component representing people's perception. Design/methodology/approach The Pressure and Release (PAR) model framework is used for basic understanding of the progression of vulnerability through identification of root causes such as: limited access to power and resources; dynamic pressures – lack of education, urbanization and demographics; and unsafe conditions such as dangerous locations. To complement PAR, the Access to Resources (ATR) model is used that expands upon the dynamics of changing decisions, options, livelihood opportunities, available resources, and choices made by the population that is impacted by disaster(s) – in time and space. Conventional risk equation: R=H x V provides community risk profile. Findings Using a working example, it is demonstrated that risk assessment can have significant influence by introducing an additional component to represent “community perception” in the fundamental risk equation. Originality/value The proposed approach: Risk (R) = Hazard (H) x Vulnerability (V) x Community Perception (cp), provides a unique and comprehensive approach to evaluate disaster risk by taking people's perception into account.
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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.006 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".