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Record W3212798268 · doi:10.1080/08865655.2021.1997629

Migration Control, the Local Economy and Violence in the Burkina Faso and Niger Borderland

2021· article· en· W3212798268 on OpenAlexvenueno aff
Kamal Donko, Martin Doevenspeck, Uli Beisel

Bibliographic record

VenueJournal of Borderlands Studies · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Identity, and Health
Canadian institutionsnot available
FundersDeutsche Forschungsgemeinschaft
KeywordsLivelihoodSettlement (finance)EconomyDiversity (politics)Political sciencePsychological interventionArmed conflictDevelopment economicsPolitical economyGeographySociologyEconomicsLaw

Abstract

fetched live from OpenAlex

The externalized European “migration management” in West Africa has technologically modernized and militarized border posts. This threatens visa-free travel, freedom of settlement and borderland economies in parts of the Economic Community of West African States (ECOWAS). It has interrupted historical mobility patterns, depleted the diversity of mobility practices and criminalized regional economies. At the same time, one can observe intensified and asymmetrical violent conflict in some of these borderlands. By taking the Kantchari-Makalondi borderland as a case study we analysed the relations between migration policies, insecurity, forced immobility and economic decline. Our observations and interviews with migrants, traders, security forces and borderlanders lead us to question conventional narratives on border control and African mobilities as a binary relation between Africa and Europe. Instead, they foreground the multiple practices of (im)mobility in these spaces: the circulation and blockage of travelers, merchandise, surveillance technologies, and military interventions and their impact on security and livelihoods.

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)0.001
metaresearch head score (Gemma)0.002
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: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.097

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.004
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.020
GPT teacher head0.322
Teacher spread0.302 · 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

Citations19
Published2021
Admission routes1
Has abstractyes

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