Two-decade bibliometric overview of publications in the <i>Journal of Knowledge Management</i>
Bibliographic record
Abstract
Purpose The purpose of this paper is to accomplish a bibliometric analysis, investigate the underlying knowledge structure, founding and development, and evolution of the Journal of Knowledge Management (JKM) through its articles published between 1997 and 2020. Design/methodology/approach A total of 1,346 research papers from JKM were selected and VantagePoint® software was used to generate bubble maps, auto-correlation maps, and matrix maps through techniques such as principal component analysis (PCA) and natural language processing (NLP). The analysis gives insights about the foundation of knowledge structure, its evolution and the development of JKM. Findings The systematic mapping of research illustrates topics emerging as new offshoots, global favourites, saturated and plateaued and reached academic maturity. The USA, the UK, Australia, Spain, Italy, China, Canada, Germany, and France have contributed the most to JKM. This paper provides a robust roadmap for future research investigation of JKM. Research limitations/implications The authors humbly admit the possibility of overlooking some research papers while evaluating and filtering the database of JKM. The research outcome summarizes 23 years, subject to information retrieval from archival files. Practical implications This research is a detailed bibliometric analysis explaining paradigm shifts in the body of knowledge of JKM. The bibliometric outcomes can act as beacons for future researchers and academicians to revisit the current trends that shape the domain of knowledge management, particularly for the JKM audience with a focus on contemporary topics of research interest. Originality/value This is a unique endeavour to accomplish a systematic bibliometric analysis of the JKM for two decades, offering insights about its structural body of knowledge through an overview of the chronology of scholarly development in the field of knowledge management.
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 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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.118 | 0.166 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".