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Record W4387216447 · doi:10.1177/23996544231200002

Post-disaster mobilities of Muslim typhoon survivors: How gendered religious preferences and discrimination shape socio-spatial exclusions in Catholic-majority Cagayan de Oro, Philippines

2023· article· en· W4387216447 on OpenAlexafffund
Christine Gibb

Bibliographic record

VenueEnvironment and Planning C Politics and Space · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change, Adaptation, Migration
Canadian institutionsUniversity of Ottawa
FundersUniversité de MontréalSocial Sciences and Humanities Research Council of CanadaFonds de Recherche du Québec-Société et CultureInternational Development Research CentreUniversity of Ottawa
KeywordsMobilitiesRelocationGender studiesPoliticsSociologyFocus groupNatural disasterTyphoonWorshipPolitical scienceCriminologyGeographyLawSocial scienceAnthropology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.382
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.078
GPT teacher head0.295
Teacher spread0.217 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2023
Admission routes2
Has abstractyes

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