A Scientometric Study on Management Literature in Southeast Asia
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.076 | 0.138 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".