MétaCan
Menu
Back to cohort
Record W4281675954 · doi:10.26443/jiows.v6i1.119

Plurilocal Communities in the Indian Ocean World

2022· article· en· W4281675954 on OpenAlexvenueno aff
Martin Sláma, Iain S. Walker

Bibliographic record

VenueThe Journal of Indian Ocean World Studies · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicSocioeconomic Development in Asia
Canadian institutionsnot available
FundersFritz Thyssen StiftungDeutsche Forschungsgemeinschaft
KeywordsDiasporaCohesion (chemistry)Indian oceanCoherence (philosophical gambling strategy)Economic geographySouth asiaCommunity cohesionGeographySpace (punctuation)MobilitiesTheme (computing)SociologyPolitical scienceGender studiesSocial scienceAnthropologyOceanographyComputer scienceGeologyLaw

Abstract

fetched live from OpenAlex

This special issue is the second of two that have emerged from a conference in Halle, Germany, in September 2019 with the theme ‘Us and them: Diasporas for others in the Indian Ocean.’1 In the introduction to the previous issue, in which we conceptualized the Indian Ocean as a diasporic space, we concluded by observing that a ‘diaspora for others’ could be characterised as plurilocal, ‘a network of spatially dispersed and geographically overlapping communities that exhibit a dynamic cohesion that is rooted in historical configurations, spatial particularities and contemporary practices.’2 Plurilocality distinguishes these diasporas from spatially dispersed groups which may be described as diasporas, but which display no real social cohesion and are simply multi-sited. Plurilocality is the infrastructure of both intra- and transnational phenomena in the contemporary world, and characterises diasporas for others, diasporas that are spatially dispersed social formations that exhibit a degree of commonality and coherence, whether cultural or social. In particular, it is through this infrastructure that transnational connections and identities in the Indian Ocean can be explored.

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)0.010
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
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 head0.051
GPT teacher head0.325
Teacher spread0.274 · 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.

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

Citations1
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of Indian Ocean World StudiesSame topicSocioeconomic Development in AsiaFrench-language works237,207