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Record W3190825273 · doi:10.1080/08865655.2022.2031253

Insecurity, Informal Trade and Timber Trafficking in the Gambia/Casamance Borderlands

2022· article· en· W3190825273 on OpenAlexvenueno aff
Martin Evans

Bibliographic record

VenueJournal of Borderlands Studies · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicAfrican Studies and Geopolitics
Canadian institutionsnot available
FundersSchool of Oriental and African Studies, University of LondonBritish AcademyChatham House
KeywordsContext (archaeology)ColonialismFrontierGovernment (linguistics)Independence (probability theory)Natural resourceInformal sectorGeographyDevelopment economicsPolitical scienceEconomyEconomic growthEconomicsArchaeology

Abstract

fetched live from OpenAlex

The Gambia’s long frontier with Casamance, southern Senegal, has historically been porous allowing informal cross-border trade to flourish. With context from colonial times, the paper examines the post-independence period, during which flows of agricultural and forest products mainly from Casamance into The Gambia have continued, while processed foods and manufactured goods have been traded in the other direction. Certain flows have become pathological since the Casamance rebellion began in 1982, with natural resources being traded by both Senegalese government and separatist forces, and arms trafficked to the latter partly through Gambian channels. With the conflict now of low intensity though not resolved, continued illegal timber exploitation in Casamance driven mainly by international actors is becoming more environmentally destructive and locally divisive. The paper argues that informal cross-border trade has long been bound up with insecurity at local, national, transnational and international levels, and that contemporary dynamics show some historical continuities.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.006
Scholarly communication0.0040.002
Open science0.0000.003
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 head0.032
GPT teacher head0.328
Teacher spread0.296 · 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 designQualitative
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

Citations9
Published2022
Admission routes1
Has abstractyes

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