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Record W3174296211 · doi:10.1080/08865655.2021.1943494

Respatializing Federalism in the Horn’s Borderlands: From Contraband Control to Transnational Governmentality

2021· article· en· W3174296211 on OpenAlexvenueno aff
Daniel K. Thompson

Bibliographic record

VenueJournal of Borderlands Studies · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsnot available
FundersDirectorate for Social, Behavioral and Economic SciencesNational Science Foundation of Sri LankaWenner-Gren Foundation
KeywordsGovernmentalityOpposition (politics)DiasporaSovereigntyPolitical economySomaliCorporate governancePolitical scienceDeportationState (computer science)DecentralizationFederalismTransnational governanceTransnationalismSociologyLawEconomicsImmigration

Abstract

fetched live from OpenAlex

Analysts documenting the proliferation of border controls amidst the global War on Terror have highlighted recent extensions of state sovereignty into new geographies. Such shifts are largely driven by Western states’ security concerns as they partner with governments in Africa and other migrant-sending spaces to stem migration. In eastern Ethiopia, however, new dynamics of border securitization have facilitated African politicians’ efforts to extend governance in the opposite direction. This article traces how authorities in Ethiopia’s Somali Regional State (SRS) between 2010 and 2018 instrumentalized the decentralization of border control in attempts to both address the persistence of “contraband” border trade and tax evasion, and solve a longstanding security problem: opposition activities among diaspora Somalis. Theoretically connecting a “borderlands as resources” framework to conceptions of transnational governmentality, this study analyzes how the governance of trade at national and subnational borders may enable efforts to regulate diaspora groups.

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 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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.429
Threshold uncertainty score0.913

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.029
GPT teacher head0.337
Teacher spread0.308 · 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 teacher head, 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

Citations22
Published2021
Admission routes1
Has abstractyes

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