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Record W2123250190 · doi:10.1093/jrs/feu023

Can Global Refugee Policy Leverage Durable Solutions? Lessons from Tanzania's Naturalization of Burundian Refugees

2014· article· en· W2123250190 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Refugee Studies · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Development and Aid
Canadian institutionsCarleton University
FundersUniversity of Dar es Salaam
KeywordsRefugeeTanzaniaNaturalizationLeverage (statistics)PoliticsPolitical scienceContext (archaeology)Development economicsEconomic growthEconomicsGeographySocioeconomicsLaw

Abstract

fetched live from OpenAlex

When Tanzania announced its willingness to naturalize some of the 220,000 Burundian refugees it had hosted since 1972, this became a test of a new global policy on protracted refugee situations and its ability to leverage durable solutions for refugees. This article examines the impact of global policy on naturalization in Tanzania, and argues that while global policy partially contributed to the formulation and early implementation of Tanzania's naturalization policy, it has not been able to ensure the full implementation of the policy in light of increased domestic opposition to local integration. In contrast, a range of domestic factors, especially within Tanzanian politics, more fully explain the formulation and uneven implementation of the naturalization policy. As such, the case of Tanzania illustrates the challenges associated with implementing global refugee policy in a domestic context and underscores the importance of ongoing political analysis in the future study and practice of global refugee policy.

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.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.729
Threshold uncertainty score0.529

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.035
GPT teacher head0.370
Teacher spread0.335 · 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