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Record W4281885472 · doi:10.26443/jiows.v6i1.121

Indian Ocean Trade and Emerging Pathways of Mobility in Neoliberal Zanzibar

2022· article· en· W4281885472 on OpenAlexvenueno aff
Akbar Keshodkar

Bibliographic record

VenueThe Journal of Indian Ocean World Studies · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Maritime and Colonial Histories
Canadian institutionsnot available
Fundersnot available
KeywordsNeoliberalism (international relations)ProsperityFutures contractColonialismCapital (architecture)Government (linguistics)Political economyPolitical scienceEconomicsGeographyLaw

Abstract

fetched live from OpenAlex

Indian Ocean trade historically directed ‘routes’ for merchants and traders to frame their ‘roots’ in Zanzibar. It facilitated access to different forms of social capital for imagining new trajectories of hope and constructing more meaningful futures. However, colonial rule and subsequent policies of the socialist revolutionary government severely restricted the mobility of Zanzibaris and their engagement in Indian Ocean trade, initiating a new era of uncertainty. As free-market policies have revived since the mid-1980s under a renewed framework of neoliberalism, this article examines how Zanzibaris, increasingly finding themselves in conditions of involuntary immobility, are trying to participate today in mercantile activities that brought prosperity in the past in hopes of securing a brighter future. The article explores how the efforts of Zanzibari merchants and traders to engage in trans- national Indian Ocean trade provides, for these limited groups of individuals, access to envisioning different pathways of socioeconomic mobility in the neoliberal era. The article contends that engagement in Indian Ocean trade, through support from transnational and diasporic networks, facilitates access to new routes of mobility to situate one’s roots in Zanzibar, while the situation for the majority of Zanzibaris continues to deteriorate under neoliberalism today.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.136
Threshold uncertainty score0.779

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.021
GPT teacher head0.278
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 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

Citations0
Published2022
Admission routes1
Has abstractyes

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