Global ranking of knowledge management and intellectual capital academic journals
Bibliographic record
Abstract
Purpose The purpose of this paper is to develop a global ranking of knowledge management and intellectual capital academic journals. Design/methodology/approach An online questionnaire was completed by 233 active knowledge management and intellectual capital researchers from 41 countries. Two different approaches: journal rank‐order and journal scoring method were utilized and produced similar results. Findings It was found that the top five academic journals in the field are: Journal of Knowledge Management, Journal of Intellectual Capital, Knowledge Management Research and Practice, International Journal of Knowledge Management, and The Learning Organization. It was also concluded that the major factors affecting perceptions of quality of academic journals are editor and review board reputation, inclusion in citation indexes, opinion of leading researchers, appearance in ranking lists, and citation impact. Research limitations/implications This study was the first of its kind to develop a ranking system for academic journals in the field. Such a list will be very useful for academic recruitment, as well as tenure and promotion decisions. Practical implications The findings from this study may be utilized by various practitioners including knowledge management professionals, university administrators, review committees and corporate librarians. Originality/value This paper represents the first documented attempt to develop a ranking of knowledge management and intellectual capital academic journals through a survey of field contributors.
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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 | 0.010 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.030 | 0.026 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".