Assessing the distance effects on motivations of emergency volunteers responding to wildfire events in protected areas
Bibliographic record
Abstract
Emergency volunteers play a critical role in responding to extreme climatic events. Overlooking their motivations may undermine the effectiveness of emergency response efforts. However, there remains a significant research gap in understanding emergency volunteers’ motivations and the influence of distance factors. To address this gap, this study employed the revised Volunteer Functions Inventory (VFI) to analyze the motivations of 345 emergency volunteers who participated in the wildfire rescue at the Jinyun Mountain Nature Reserve in China. The findings revealed that emergency volunteers’ motivations were primarily driven by Values, followed by Social, Understanding, Enhancement, and Protective motivations, with Career motivations being the least influential. Notably, emergency volunteers residing farther from the wildfire-affected areas prioritized ecological conservation over social connections or career interests. Additionally, our study found that age, income, educational level, and experience have a significant impact on emergency volunteers’ motivations. This study represents the first attempt to quantitatively assess emergency volunteers’ motivations in wildfire rescue, revealing spatial disparities in motivations and integrating the social resilience perspective into extreme event management.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".