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Religion Influence on Disaster Risk Reduction: A case study of Serbia

2023· preprint· en· W4316653920 on OpenAlexaff
Vladimir M. Cvetković, Saša Romanić, Hatidža Beriša

Bibliographic record

VenuePreprints.org · 2023
Typepreprint
Languageen
FieldPsychology
TopicLeadership, Courage, and Heroism Studies
Canadian institutionsInternational Institute for Sustainable Development
Fundersnot available
KeywordsReligiosityFatalismNatural disasterPopulationCivilizationBelief in GodDevelopment economicsEnvironmental ethicsPolitical sciencePsychologySocial psychologyGeographySociologyLawEconomicsEpistemologyDemography

Abstract

fetched live from OpenAlex

Human perception of nature and God have always been inextricably linked. In order to understand nature and its inherent processes, including various natural hazards, the reasons for their origin were often attributed to God's will, suffering for sin and the similar. Fear of material and human losses prompted a man to pray and offer sacrifices/gifts and other rituals to appease the "wrath of the gods". The progress of civilization and technology has not alleviated the destruction and trauma that natural disasters inflict on all aspects of social life. A major obstacle to this is the exponential population growth in vulnerable areas. The frequency of natural disasters and the fatalistic attitudes that limit the effective fight against them have motivated religious communities and individuals to cooperate with international and international organizations and institutions to reduce the risk of local disasters. Believers thus receive the necessary psychological and financial assistance and support from religious communities during all phases of disaster management. Therefore, the subject of this paper is a comprehensive examination relationship between the degree of religiosity of the population and how this connection impacts the policy of reducing disaster risk. The aim of the research is to scientifically describe the nature of the relationship between the degree of religiosity of citizens and different segments of disaster risk reduction.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.004

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 head0.277
GPT teacher head0.427
Teacher spread0.150 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2023
Admission routes1
Has abstractyes

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