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Record W2728392418 · doi:10.1080/11926422.2017.1341844

Security sector reform in Haiti since 2004: limits and prospects for public order and stability

2017· article· en· W2728392418 on OpenAlexafffund
Gaëlle Rivard Piché

Bibliographic record

VenueCanadian Foreign Policy Journal · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicPeacebuilding and International Security
Canadian institutionsCarleton University
FundersInternational Development Research Centre
KeywordsContext (archaeology)Security sector reformPolitical scienceOrder (exchange)Public sectorState (computer science)Software deploymentNational securityPublic securityPublic administrationInternational securityIntervention (counseling)Economic growthBusinessPoliticsGeographyLawEconomicsEngineering

Abstract

fetched live from OpenAlex

Security sector reform (SSR) has been at the core of the international intervention in Haiti since the mid-1990s. Following the deployment of MINUSTAH in 2004, the scope of SSR varied, with more or less consideration for non-state actors, and influenced public order and violence in the country. Under President René Préval (2006–2009), efforts were made to address the role of non-state actors in the production of public order and security provision at the local level, with positive impact on the level of public order in Port-au-Prince. After the 2010 earthquake and the election of Michel Martelly, however, this approach was mostly abandoned. International donors refocused their assistance in the security sector on the development of the national police. By 2014, despite continued international presence, Haiti registered the highest level of homicides since 2007. This article contends that state-centric SSR is unlikely to improve security and stability in this context since it ignores parts of the Haitian security sector.

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)0.003
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.110
Threshold uncertainty score0.219

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.005
Scholarly communication0.0060.004
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.054
GPT teacher head0.327
Teacher spread0.273 · 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

Citations8
Published2017
Admission routes2
Has abstractyes

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