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Record W2611578056

Regional Migration Governance in the African Continent. Current State of Affairs and the Way Forward

2016· article· en· W2611578056 on OpenAlexaff
Lorenzo Fioramonti, Christopher Changwe Nshimbi

Bibliographic record

VenueSSRN Electronic Journal · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Issues in South Africa
Canadian institutionsInstitute on Governance
Fundersnot available
KeywordsCorporate governancePolitical scienceRegionalism (politics)European unionGovernment (linguistics)GermanRegional scienceState (computer science)GeographyDevelopment economicsEconomic growthInternational tradeBusinessEconomicsPoliticsDemocracy
DOInot available

Abstract

fetched live from OpenAlex

This study has three objectives: a) to provide an overview over the current migration policies of the AU as well as of selected RECs, in particular the EAC, the ECOWAS and the SADC; b) to develop a set of criteria and recommendations to further develop and improve these policies and their implementation; c) to advise external actors, in particular the German government and the European Union (EU), as to how best support such policies. The study provides an in-depth analysis of existing migration policies and practices at the African continental level, as well as in three key regions: the EAC, ECOWAS and SADC. After contextualizing the issue of migration within the broad literature on regionalism, the paper discusses regional migration governance in Africa by focusing on legislations, policies and practices across the continent, with a particular focus on West, Eastern and Southern Africa. It then goes on to summarize the implications of the regional experiences for migration management in Africa. It concludes with a presentation of potential scenarios and policy recommendations for migration governance in Africa.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.021
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.005
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.010
GPT teacher head0.267
Teacher spread0.257 · 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 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

Citations11
Published2016
Admission routes1
Has abstractyes

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Same venueSSRN Electronic JournalSame topicLegal Issues in South AfricaFrench-language works237,207