Post-disaster mobilities of Muslim typhoon survivors: How gendered religious preferences and discrimination shape socio-spatial exclusions in Catholic-majority Cagayan de Oro, Philippines
Bibliographic record
Abstract
Natural hazards don't care who you worship. However, the evacuation camps, transitional housing sites and relocation sites aimed at helping disaster survivors do. Empirically, this paper explains a puzzle in which Muslim survivors of Typhoon Sendong in the Philippines were all but absent in official post-disaster spaces of this Catholic-majority country. Based on qualitative interviews, focus groups and site visits, I identify two exclusionary mechanisms: (1) prejudices, preferences and practicalities, and (2) socio-spatial design of official post-disaster spaces. This paper argues that by studying Muslim survivors' post-disaster mobilities, we see that discrimination along the lines of religion, as it plays out in everyday gendered religious socio-spatial practices, repels survivors from accessing evacuation camps and other post-disaster spaces. This is important for two related reasons. One, these humanitarian spaces claim to be inclusive yet, in practice, deter would-be migrants on the basis of religion. Two, religiously-informed gender relations shape the politics of disaster recovery processes, which further exacerbate inequities post-disaster.
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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 | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".