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Record W4375798959 · doi:10.26686/wgtn.17142425.v1

Strengthening Capacities Towards a Resilient Future: The case of Sexual and Gender Minorities in Tacloban City, Philippines after the 2013 Typhoon Haiyan

2019· dissertation· en· W4375798959 on OpenAlexfundno aff
Maria Theresa Castro

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicClimate Change, Adaptation, Migration
Canadian institutionsnot available
FundersNew Zealand Aid ProgrammeMinistère de la Défense NationaleCatholic Relief ServicesMinistry of Foreign Affairs and Trade, New ZealandNew Zealand Foreign Affairs and TradeVictoria University of WellingtonNew Zealand Government
KeywordsDisaster risk reductionThematic analysisFocus groupPolitical scienceContext (archaeology)Psychological interventionPublic relationsGeographySociologyPsychologyQualitative researchEnvironmental planningSocial science

Abstract

fetched live from OpenAlex

<p>This thesis explores the experiences, challenges, and roles of people who identify as sexual and gender minorities in the context of disaster risk reduction management and practices. In recent decades, national and international institutions have exerted substantial efforts to reduce disaster risk and strengthen disaster management. In response to the increasing number and magnitude of weather events and climate impacts worldwide, this thesis takes into consideration the significance of equity and inclusion in different stages of disaster risk reduction management (DRRM). It focuses particularly on recovery and rehabilitation activities that can build resilience towards disasters. As a case study, it investigates the post-disaster relief and rehabilitation efforts carried out in Tacloban City after the wrath of Typhoon Haiyan, locally known as Typhoon Yolanda, in November 2013. The tropical storm left thousands of casualties, with millions of people homeless and/or displaced, forcing them to live in a temporary or permanent shelter. The research approach was transformative and informed by principles of participatory action research. The methodology followed the appreciative inquiry process of the 4 D’s - Discovery, Dream, Design, Destiny. This approach was strength-based and involved working with local, community organisations and government officials. Data were collected using key-informant interviews, semi-structured interviews, focus group discussions (including some participatory techniques), and a structured survey of residents in the city and permanent shelters. These data were analysed using thematic analysis. The study reveals how post-disaster interventions and strategies after Typhoon Haiyan reflected heterosexist assumptions, which undermined recovery and rehabilitation efforts. These assumptions, and the wider heteropatriarchal system of which they are a part, served to magnify some existing inequalities, vulnerabilities, and social exclusion based on gender and sexuality. This social system, however, also facilitated the development and/or realisation of endogenous skills and capacities of gender minorities. As such, they were able to take leadership roles and carry out recovery activities unavailable to heterosexual residents. In light of this data, I argue that people who identify as sexual gender minorities are potentially a neglected resource in times of disaster and recovery. If their capabilities were recognised and integrated into DRRM policies and practice, efforts could be enhanced to promote recovery and resilience in hazard-affected communities. Additional work is also needed to challenge the wider system of heteropatriarchy outside of times of disaster to minimise further marginalisation of gender sexual minorities during post-disaster relief and rehabilitation. Overall, this research contributes towards the development of a shared understanding about how a community's capacities and/or strengths can be improved and utilized within disaster risk reduction management and practices by focusing on sexuality and gender.</p>

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)0.001
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.742
Threshold uncertainty score0.918

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.079
GPT teacher head0.321
Teacher spread0.242 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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