Tackling Wicked Problems in Risk and Crisis Communication Through Design Thinking: Insights From the ICRCC Conferences 2024 and 2025
Bibliographic record
Abstract
This paper compares two design thinking workshops conducted at the International Crisis and Risk Communication Conferences (ICRCC) in 2024 and 2025, each tackling a distinct yet equally wicked communication challenge. The first workshop addressed internal cyber risk communication, exploring how to shift employee behavior and internalize security messages in organizational contexts. The second workshop focused on governmental crisis readiness, co-developing strategies to improve political and administrative response to crises such as cyberattacks, political scandals, and public emergencies. Each workshop utilized human-centered design approaches – emphasizing empathy, co-creation, and rapid prototyping – anchored in the IDEA model and enriched by tools such as persona development, message mapping, and readiness canvases. This comparative analysis demonstrates the versatility of design thinking in navigating complex, under-researched issues in risk and crisis communication, and offers actionable insights for theory, practice, and pedagogy in strategic communication planning.
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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 | 0.051 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.009 | 0.023 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".