Road to Resettlement: Understanding Post-disaster Relocation and Resettlement Challenges and Complexities Through a Serious Game
Bibliographic record
Abstract
Abstract Resettlement and relocation are among the most difficult policies to put into practice, but they may be the best ways to minimize future risks to settlements exposed to natural hazards both before and after disaster events. As climate-related disasters and forced migration become increasingly common worldwide, governments, humanitarian or development actors, and policymakers must now prioritize the implementation of a dignified and effective resettlement program as part of their planning and management responsibilities. Much of this effectiveness depends on the stakeholders and beneficiaries’ understanding and knowledge of the different resettlement phases, culture and customs of affected populations, activities, and the associated implementation challenges, costs, and benefits. Serious games are used in a variety of contexts to increase awareness, train and build capacity in stakeholders and beneficiaries. This article presents a serious game developed to educate practitioners, local agencies, students, and the public to understand the complexities and challenges involved in a successful resettlement. The game is based on a real proposed resettlement project initiated in the Chiradzulu District in southern Malawi after Cyclone Freddy in March 2023, which caused widespread flooding and landslides, forcing some villages to relocate permanently. The progression in the Road to Resettlement Game consists of six primary levels: land and site preparation, housing and livelihood, water, sanitation, and hygiene, health, education, and protection. These levels are meant to be completed in a sequence that adheres to the principles of resettlement. By engaging in the serious table-top board game, players gain an understanding of the resettlement activities, their sequence, and the associated practical (technical and social) and financial challenges.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".