Meta-analysis of scientometric research of knowledge management: discovering the identity of the discipline
Bibliographic record
Abstract
Purpose – The purpose of this study is to conduct a meta-analysis of prior scientometric research of the knowledge management (KM) field. Design/methodology/approach – A total of 108 scientometric studies of the KM discipline were subjected to meta-analysis techniques. Findings – The overall volume of scientometric KM works has been growing, reaching up to ten publications per year by 2012, but their key findings are somewhat inconsistent. Most scientometric KM research is published in non-KM-centric journals. The KM discipline has deep historical roots. It suffers from a high degree of over-differentiation and is represented by dissimilar research streams. The top six most productive countries for KM research are the USA, the UK, Canada, Germany, Australia, and Spain. KM exhibits attributes of a healthy academic domain with no apparent anomalies and is progressing towards academic maturity. Practical implications – Scientometric KM researchers should use advanced empirical methods, become aware of prior scientometric research, rely on multiple databases, develop a KM keyword classification scheme, publish their research in KM-centric outlets, focus on rigorous research of the forums for KM publications, improve their cooperation, conduct a comprehensive study of individual and institutional productivity, and investigate interdisciplinary collaboration. KM-centric journals should encourage authors to employ under-represented empirical methods and conduct meta-analysis studies and should discourage conceptual publications, especially the development of new frameworks. To improve the impact of KM research on the state of practice, knowledge dissemination channels should be developed. Originality/value – This is the first documented attempt to conduct a meta-analysis of scientometric research of the KM discipline.
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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 | 0.170 | 0.439 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.026 |
| Bibliometrics | 0.074 | 0.064 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".