Developing a Crowdsourcing-Based Disaster Relief Model Based on Public Participation
Bibliographic record
Abstract
These studies aim to accomplish the following: (1).Examining and developing a crowdsourcing-based model for community disaster preparedness, (2).Outline the model for disaster management in the region and the structure and procedure that will be used to put it into effect in local communities.The R&D method behind this study aimed to undertake a thorough analysis before constructing a brand-new framework for the concept.The province of Lampung was chosen because it has both an established regulatory framework for disaster management and innovative technical programs.New processes and workflows for volunteer coordination were defined and tested in collaboration with the Indonesia Community disaster and other volunteer group as the end user, and a platform was developed to support them.Increased situational awareness in real-time via unprompted input from many users is a key benefit of such a system.Not only that, but unannounced participants can join the conversation simply by signing up for a website or portal.The crowdtasking system will not fully trust spontaneous volunteers until the community disaster staff has verified their profiles.As a result, they are given less important and less taxing activities than pre-registered volunteers, and their observations are given less weight.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".