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Record W4401619360 · doi:10.1016/j.ijdrr.2024.104764

Assessing the multidimensional nature of flood and drought vulnerability index: A systematic review of literature

2024· review· en· W4401619360 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueInternational Journal of Disaster Risk Reduction · 2024
Typereview
Languageen
FieldEnvironmental Science
TopicHydrology and Drought Analysis
Canadian institutionsGlobal Institute for Water SecurityUniversity of CalgaryUniversity of Saskatchewan
FundersGlobal Institute for Water Security, University of SaskatchewanCanada First Research Excellence FundUniversity of Saskatchewan
KeywordsVulnerability (computing)Flood mythIndex (typography)Environmental scienceEnvironmental planningGeographyComputer scienceArchaeologyComputer security

Abstract

fetched live from OpenAlex

Vulnerability to floods and droughts is a complex and multidimensional phenomenon influenced by various factors. This systematic review paper focuses on communities’ vulnerability to floods and droughts. It presents an overview of the current knowledge on the topic, including definitions and conceptual frameworks related to vulnerability. The study synthesizes existing literature from various disciplines, including hydrology, climatology, geography, and social sciences, to identify key factors contributing to vulnerability and its impacts on communities, infrastructure, and ecosystems. Through a comprehensive analysis of 83 articles published between 2010 and 2023, this paper identifies themes, methodologies, and knowledge gaps in flood and drought vulnerability assessment. The findings reveal that vulnerability to floods and droughts depends on a range of factors, including physical exposure, socioeconomic status, governance, and cultural values. Most of the published articles have focused on regional-scale studies. There has been an increase in the number of vulnerability studies addressing this issue after 2019. Among the various methods analyzed, min-max normalization (52 % of articles) and equal weighting (27 %) were the most frequently used data normalization and aggregation methods. However, the paper identifies a significant research gap in the lack of sensitivity analysis or validation of the indices developed based on the most common parameters, such as population density, gender, income, and precipitation levels. It also emphasizes the need for true transdisciplinary approaches for a comprehensive assessment of flood and drought vulnerabilities. The systematic review concludes with a synthesis of core vulnerability indicators and recommendations for future research and policy directions aimed at reducing the vulnerability of communities to these natural hazards. • Vulnerability to flood and drought is multifaceted and influenced by various factors. • The review combines knowledge from across disciplines to better understand vulnerability. • Findings reveal key factors contributing to vulnerability and research gaps. • Vulnerability indices must include sensitivity analyses as part of their deployment. • Vulnerability must be considered through a transdisciplinary lens.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.110
Threshold uncertainty score0.560

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.343
Teacher spread0.332 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it