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Record W2163061726 · doi:10.5334/sta.am

Reviewing the Justice and Security Hub Modality as Piloted in Liberia

2012· article· en· W2163061726 on OpenAlexvenueno aff
Rory Keane

Bibliographic record

VenueStability International Journal of Security and Development · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicPeacebuilding and International Security
Canadian institutionsnot available
Fundersnot available
KeywordsPeacebuildingPeacekeepingGovernment (linguistics)DemocracyEconomic JusticePolitical sciencePublic administrationSecurity councilThe RepublicEconomic growthLawPoliticsEconomics

Abstract

fetched live from OpenAlex

‘The Republic of Bangui’ or ‘the Republic of Monrovia’ are phrases we sometimes hear from practitioners to describe post conflict countries where very few services exist outside the capital city. This is especially the case for security – the critical public good in post conflict countries. In response to the need to bring security services closer to the citizens who often need them most, the Government of Liberia and the United Nations are piloting a new approach financed by the UN Peacebuilding Fund (PBF) – the so-called ‘Justice and Security Hubs’. The donor community and the United Nations are watching closely. If this works, there is indication from UN officials that the model could potentially be replicated in other settings such as the East of the Democratic Republic of the Congo (DRC), Haiti and the northern states of South Sudan. If the hub concept is capable of being adapted and successful elsewhere, the United Nations will not only have added a new instrument to its peacekeeping toolkit but will also firmly demonstrate how the UN Peacebuilding Fund can in essence be catalytic in fostering long-term and comprehensive approaches to peacebuilding. This practice note outlines the process of developing and constructing the first hub in Liberia, which is due to be partly operational by the end of 2012, and provides a prognosis on its chances for success.

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)0.006
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.544
Threshold uncertainty score0.398

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.048
GPT teacher head0.352
Teacher spread0.304 · 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 designObservational
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
Published2012
Admission routes1
Has abstractyes

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