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Record W2413926299 · doi:10.5539/jsd.v9n3p136

Sufism and Behavior of Maritime Economic Community (Case Study on Pakkaja Community in South Sulawesi)

2016· article· en· W2413926299 on OpenAlexvenueno aff
Eymal B. Demmallino, Mukti Ali, Abd. Qadir Gassing, Munsi Lampe, La Nalefo, Nurbaya Busthanul

Bibliographic record

VenueJournal of Sustainable Development · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicIslamic Finance and Communication
Canadian institutionsnot available
Fundersnot available
KeywordsBackwardnessSufismMuslim communityIslamizationEconomyGeographyAncient historySociologyHistoryIslamEconomic growthEconomicsArchaeology

Abstract

fetched live from OpenAlex

This research is motivated by a concern to the maritime community in Indonesia and South Sulawesi in particular, which is up to now still very behind compared with other communities on the mainland or degenerate far backward compared with the maritime community in the past royal era (eighth century ~ XVII century: Sriwidjaya, Majapahit, and Gowa~Makassar). This study aims to reveal the negative effect on classical Sufism of maritime community economic behavior, by taking the case to the Pakkaja Community. This study uses Verstehen method is done by "reversed burden of proof". The results showed that the backwardness of maritime community, in particular the Pakkaja Community is rooted in the mentality of weak intention characterized by low productivity, dependence excessively and consumptive lifestyles in their lives, is a negative deflection of classical Sufism, which stuck very strong since early Islamization of the archipelago and Bugis ~ Makassar in particular.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.115

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.033
GPT teacher head0.300
Teacher spread0.267 · 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

Citations3
Published2016
Admission routes1
Has abstractyes

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