Researching the Resolution of Post-Disaster Displacement: Reflections from Haiti and the Philippines
Bibliographic record
Abstract
Researching the resolution of post-disaster displacement raises a range of under-examined challenges. This article contributes to the literature on research methods and forced migration by analysing experiences conducting two policy research projects that employed a mixture of qualitative and quantitative methods to explore the pursuit of ‘durable solutions’ to post-disaster displacement in Haiti and the Philippines. Many scholars are highly critical of how policy concepts and categories have sometimes unthinkingly shaped research on displacement, but the views of policy researchers and researcher-practitioners are under-represented in this conversation. This article seeks to advance discussions on the relationship between research, policy and practice in the field of forced migration by reflecting on efforts to undertake thoughtful policy research on durable solutions while making the very notion of durable solutions and tools such as the Inter-Agency Standing Committee (IASC) Framework on Durable Solutions for Internally Displaced Persons central objects of investigation. In particular, it explores four key issues: the structure of policy research partnerships; implications of different approaches to conceptualizing displacement and durable solutions; the challenge of understanding displacement and durable solutions in relation to broader and pre-disaster politics, conditions and concerns; and the timing of studies on durable solutions.
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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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.031 | 0.034 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".