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Record W2770075510 · doi:10.2495/safe-v7-n3-294-302

A study of motivational aspects initiating volunteerism in disaster management in Germany

2017· article· en· W2770075510 on OpenAlexvenueno aff
Pablo Holwitt, Stefan Strohschneider, Robert Zinke, Sarah Kaiser, Ines Kranert, Andrew M. Linke, Margaret S. Mahler

Bibliographic record

VenueInternational Journal of Safety and Security Engineering · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsnot available
FundersBundesministerium für Bildung und Forschung
KeywordsPsychologyApplied psychologyEmergency managementEnvironmental healthMedicinePolitical science

Abstract

fetched live from OpenAlex

Volunteer work is an important aspect that influences a society´s ability to cope with different kinds of security threats and disasters.However, the motivational and social sources that drive civil engagement in the field of disaster management are not understood very well.If societies want to preserve or increase their resilience and thus reduce their vulnerability to disasters, volunteerism is an important topic to look into.In order to foster voluntary engagement in disaster management, it is essential to both understand the motivational sources that drive volunteers and establish appropriate conditions for future voluntary engagement.In this article, motivations of volunteers in three regions of Germany are analysed using a theoretical model that builds on the works by psychologists Dörner and Bischof.The model considers volunteerism as a way of catering to three basic needs of human beings: the need for affiliation, the need for certainty and the need for control.This model is applied to data gathered from unstructured and semi-structured interviews with volunteers and professionals working in the field of disaster management in Germany.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.249
Threshold uncertainty score0.300

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.017
GPT teacher head0.309
Teacher spread0.292 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations8
Published2017
Admission routes1
Has abstractyes

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