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Record W4309089755 · doi:10.3390/jrfm15110507

A Scientometric Study on Management Literature in Southeast Asia

2022· article· en· W4309089755 on OpenAlexvenueno aff
Egi Arvian Firmansyah, Hairunnizam Wahid, Ardi Gunardi, Fahmi Ali Hudaefi

Bibliographic record

VenueJournal of risk and financial management · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
Fundersnot available
KeywordsScopusContext (archaeology)Relevance (law)PublishingNoveltyPolitical scienceLibrary scienceSubject (documents)Web of scienceIslamAnalyticsGeographyData scienceComputer scienceLawPsychologyArchaeology

Abstract

fetched live from OpenAlex

This study employs bibliometric analysis, i.e., a kind of data analytics for evaluating scholarly publications, to evaluate journal publishing management issues in the Southeast Asian context. A total of 500 Scopus-indexed documents from Jurnal Pengurusan were sampled. The finding reveals that Malaysia is the most prominent country in terms of author affiliation, country performance, and keyword appearance. The collaboration among the authors of the sampled journal is primarily from the Asian continent, with a few from Australia. The topics of this journal have incrementally evolved from conventional to contemporary issues. This journal has made substantial contributions to the subject of Islamic finance and business, which is congruent with Malaysia’s role as a global center of Islamic finance. In addition, some contemporary subjects, such as blockchain, metaverse, and fintech, have emerged, demonstrating the relevance of this journal coverage to the contemporary management issues occurring in the financial markets worldwide. This study provides a critical novelty in the assessment of scholarly publications on management issues in the Southeast Asian context with Jurnal Pengurusan as the case.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.729
Threshold uncertainty score0.648

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.008
GPT teacher head0.217
Teacher spread0.209 · 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 designObservational
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

Citations11
Published2022
Admission routes1
Has abstractyes

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