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Record W2170931827 · doi:10.1093/eurpub/ckq032

Joint EU Resettlement Programme: the health of refugee and humanitarian arrivals

2010· article· en· W2170931827 on OpenAlexaboutno aff
Claire E. Brolan

Bibliographic record

VenueEuropean Journal of Public Health · 2010
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsnot available
FundersEuropean CommissionAtlantic Philanthropies
KeywordsRefugeeJoint (building)Political scienceEngineeringLawCivil engineering

Abstract

fetched live from OpenAlex

On 2 September 2009, the European Commission proposed the establishment of a Joint EU Resettlement Programme.1 The aim of the programme, led by an EU-wide Resettlement Expert Group, is to expand EU resettlement of refugees and humanitarian entrants, already determined by the United Nations High Commissioner for Refugees (UNHCR), to be in genuine need of international protection. In 2010, UNHCR estimates that around 747 000 persons worldwide are in need of resettlement.2 This number is increasing without corresponding growth in the number of available resettlement places.3 Further, host countries in the developing world, with limited resources, are overburdened and simply cannot integrate large numbers of refugees into their own expanding populace. The Joint EU Resettlement Programme therefore progresses the EU’s ‘greater solidarity’ to third countries that are overloaded, while improving coordination of EU external policies and credibility in international affairs more generally. The USA, Canada and Australia are the three major resettlement nations. Of the 65 850 refugees resettled worldwide in 2008, just 6.7% (4378) were resettled in 10 EU …

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)0.006
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.010
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.002

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 head0.119
GPT teacher head0.372
Teacher spread0.253 · 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 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

Citations3
Published2010
Admission routes1
Has abstractyes

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