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Record W2019549893 · doi:10.1163/17087384-12342028

From Durable Solutions to Holistic Solutions: Prevention of Displacement in Africa

2014· article· en· W2019549893 on OpenAlexvenueno aff
Dan Kuwali

Bibliographic record

VenueAfrican Journal of Legal Studies · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicHuman Rights and Development
Canadian institutionsnot available
Fundersnot available
KeywordsInternally displaced personDignityLivelihoodHuman rightsDisplaced personPolitical scienceGood governanceInternational lawBusinessDevelopment economicsCorporate governanceLawEconomic growthRefugeeEconomicsGeography

Abstract

fetched live from OpenAlex

Abstract The problem of internally displaced persons (IDPs) is prevalent in Africa due to conflicts, development projects, man-made as well as natural disasters that are commonplace on the continent. The Kampala Convention, which is an innovative tool, seeks to provide African States with a roadmap towards long-term solutions to prevent and eradicate the causes of displacement. As such, African States, humanitarian agencies, the African Union and other stakeholders should go beyond durable solutions, which are reactive, and pay more attention to proactive strategies to eradicate the factors that lead to displacement. To do this, there is need to adopt a holistic approach to address the question of IDPs on the continent through a three-tiered strategy that includes; firstly, short-term strategy to protect the IDPs by providing safety and security, freedom of movement as well as basic livelihood to the IDPs; secondly, medium-term strategy to restore IDPs’ dignity and ensure adequate living conditions through return, resettlement, (re)integration, reparation, restitution and rehabilitation; and thirdly, long-term strategy to eradicate root causes of displacement and fostering an environment conducive to respect of human rights, rule of law and good governance.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.627
Threshold uncertainty score0.322

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.113
GPT teacher head0.353
Teacher spread0.240 · 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 designNot applicable
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

Citations3
Published2014
Admission routes1
Has abstractyes

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