A study of motivational aspects initiating volunteerism in disaster management in Germany
Bibliographic record
Abstract
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.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".