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Record W2388282751 · doi:10.1093/jhuman/huw007

Understanding and Addressing Vulnerability Following the 2010 Haiti Earthquake: Applying a Feminist Lens to Examine Perspectives of Haitian and Expatriate Health Care Providers and Decision-Makers

2016· article· en· W2388282751 on OpenAlexfundno aff
Evelyne Durocher, Ryoa Chung, Christiane Rochon, Matthew Hunt

Bibliographic record

VenueJournal of Human Rights Practice · 2016
Typearticle
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchMcGill University
KeywordsVulnerability (computing)ExpatriateCLARITYSociologyEquity (law)Public relationsPsychologyPolitical scienceComputer securityLaw

Abstract

fetched live from OpenAlex

Vulnerability is a central concept in humanitarian aid. Discussions of vulnerability in disaster response literature and guidelines for humanitarian aid range from considerations of a universal human vulnerability, to more nuanced examinations of how particular characteristics render individuals more or less at risk. Despite its frequent use, there is a lack of clarity about how vulnerability is conceptualized and how it informs operational priorities in humanitarian assistance. Guided by interpretive description methodology, we draw on the feminist taxonomy of vulnerability presented by Mackenzie, Rogers and Dodds (2014) to examine perspectives of 24 expatriate and Haitian decision-makers and health professionals interviewed between May 2012 and March 2013. The analysis explores concepts of vulnerability and equity in relation to the humanitarian response following the 2010 earthquake in Haiti. Participants' conceptualizations of vulnerability included consideration for inherent vulnerabilities related to individual characteristics (e.g. being a woman or disabled) and situational vulnerabilities related to particular circumstances such as having less access to health care resources or basic necessities. Participants recognized that vulnerabilities could be exacerbated by socio-political structures but felt ill-equipped to address these. The use of the taxonomy and a set of questions inspired by Hurst's (2008) approach to identifying and reducing vulnerability can guide the analysis of varied sources of vulnerability and open discussions about how and by whom vulnerabilities should be addressed in humanitarian responses. More research is required to inform how humanitarian responders could balance addressing acute vulnerability with consideration of systemic and pre-existing circumstances that underlie much of the vulnerability experienced following an acute 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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.026
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0180.023
Scholarly communication0.0080.008
Open science0.0020.010
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0030.000

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 head0.153
GPT teacher head0.439
Teacher spread0.287 · 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 source (direct Gemma or distilled Codex), 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

Citations28
Published2016
Admission routes1
Has abstractyes

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