Developing a Digital Disaster Documents System for essential documents: Perspectives of decision-makers in disaster and emergency management in Canada
Bibliographic record
Abstract
Despite a growing recognition in the literature concerning the intricate relationship between innovation as an adaptive measure to effectively achieve the overarching objectives of disaster risk reduction and resilience, limited studies have examined how social innovation can be tailored to the local context. This study fills this gap by examining decision-makers' perspectives on the Digital Disaster Documents System (D3S), which digitizes vital documents for disaster response and recovery. A web-based survey was completed by 21 decision-makers across Canada, analyzing their responses using thematic analysis and descriptive statistics. Overall, decision-makers exhibit a positive attitude toward the innovation of D3S as a means to enhance disaster preparedness. Moreover, their constructive feedback on various aspects (content, organization, and storage) of the D3S paves the way for necessary adjustments and enhancements tailored to local needs. This research underscores the necessity for social innovations in emergency and disaster preparedness, especially in ways that are inclusive and equitable.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.026 | 0.009 |
| Scholarly communication | 0.012 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".