Prefabricating marginality: long-term housing impacts of displacement in post-disaster Montserrat
Bibliographic record
Abstract
This paper investigates the long-term housing impacts of displacement and explores how these vary across disaster-affected populations. The Caribbean island of Montserrat, an overseas territory of the United Kingdom, provides an excellent setting for examining this relatively understudied topic. Following the eruption of the Soufrière Hills volcano, beginning in 1995, most Montserrat residents were displaced and the island’s south was declared an exclusion zone. The paper draws on interviews with 89 randomly selected residents, including displacees and non-displacees, and with 10 Montserratian and United Kingdom officials charged with responding to post-disaster needs. The paper seeks to understand variation in long-term housing conditions with a focus on the impact of housing type. The results show that interviewees living in housing built for, rather than by, displacees had significantly lower housing satisfaction scores, with residents of prefabricated houses reporting the lowest scores. Interviewees argued that the top-down provision of these houses was problematic due to limited local input and use of materials poorly suited to local conditions and traditions. The paper concludes by situating the findings in the context of the literature on post-disaster housing and by arguing for increased attention to how such housing is provided in terms of both process and materials.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".