Critical Response and Preparedness for Resilient Futures
Bibliographic record
Abstract
Natural disasters not only threaten the safety of individuals and communities but also disrupt the everyday life of people and their means of communication. People are highly alarmed in such situations. They do not know what to do or whom to contact, and wonder if their loved ones are safe; this causes people to depend on real-time information and assistance from each other rather than solely depending on government authorities during a disaster. Communication plays a crucial part in people’s survival in such circumstances. Currently, one-way mass notification systems mostly take place in the form of general alerts or warnings. However, people are looking for two-way communication during disasters; they want to be a part of the conversation and the solution. Everyday communication systems like Facebook, Twitter, and Instagram are perfect examples where communities provide information and assist each other and authorities during disasters. However, these do not involve the authorities in the two-way communication problem; multi-level gaps in the current emergency communication system remain. Aim To identify opportunities in the mass emergency communication system to help increase the chances of people’s safety during natural disasters like fire, flood and landslide in British Columbia. Research Questions • How might we design a two-way communication system for communities in disaster zones of British Columbia during mass emergencies like fire, flood, and landslides? • How might we leverage everyday communication systems that support communities during mass emergencies? • How might we include communication strategies specific to building Community resilience? The design solution is based on human-centered design (What Is Human-Centered Design?, n.d.) and system design (Meadows, 2009) methodologies. It aims to understand community needs and communication patterns, build a holistic knowledge of different stakeholder integrated systems, and evaluate the accessibility of the existing communication preparedness plans to design a solution. The project began by collecting and analyzing data from literature reviews, interviews, surveys, exploratory designs, and workshops to create a list of design principles. It then utilized these principles to develop a government-based universal information-sharing and community collaboration platform that integrates with Canada’s AlertReady system (Government of Canada, 2015).
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.038 | 0.004 |
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".