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The effects of stabilisation on humanitarian action in Haiti

2010· article· en· W2127763274 on OpenAlexaboutno aff
Robert Muggah

Bibliographic record

VenueDisasters · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsnot available
Fundersnot available
KeywordsPsychological interventionPromotion (chess)Political scienceNatural disasterAction (physics)Humanitarian aidNorwegianPort (circuit theory)Investment (military)State (computer science)Economic growthPublic relationsPublic administrationMedicineGeographyEngineeringLawEconomicsNursingPolitics

Abstract

fetched live from OpenAlex

Haiti is routinely characterised as an archetypical fragile state. In spite of considerable donor investment in security promotion, real and perceived safety have proven frustratingly elusive. In the years before the devastating earthquake of 12 January 2010, the country's capital, Port-au-Prince, was also the site of considerable experimentation to promote security and stability. T his paper reviews the discourse, practice and outcomes associated with three parallel stabilisation initiatives undertaken in Haiti between 2007 and 2009. Although they shared many similar objectives, the paper describes how these separate interventions mobilised very different approaches. The specific focus is on United States, United Nations and combined Brazilian, Canadian and Norwegian stabilisation efforts and their implications for humanitarian actors, including the International Committee of the Red Cross and Médecins Sans Frontières. The paper concludes with some reflections on the implications of stabilisation before and after the country's most recent natural 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)0.004
metaresearch head score (Gemma)0.005
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 score0.069
Threshold uncertainty score0.138

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.021
Scholarly communication0.0050.003
Open science0.0010.006
Research integrity0.0020.003
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 head0.012
GPT teacher head0.300
Teacher spread0.288 · 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

Citations70
Published2010
Admission routes1
Has abstractyes

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