Global ranking of knowledge management and intellectual capital academic journals: 2013 update
Bibliographic record
Abstract
Purpose The purpose of this study is to update a global ranking of knowledge management and intellectual capital (KM/IC) academic journals. Design/methodology/approach Two different approaches were utilized: a survey of 379 active KM/IC researchers; and the journal citation impact method. Scores produced by the application of these methods were combined to develop the final ranking. Findings Twenty‐five KM/IC‐centric journals were identified and ranked. The top six journals are: Journal of Knowledge Management, Journal of Intellectual Capital, The Learning Organization, Knowledge Management Research & Practice, Knowledge and Process Management and International Journal of Knowledge Management. Knowledge Management Research & Practice has substantially improved its reputation. The Learning Organization and Journal of Intellectual Capital retained their previous positions due to their strong citation impact. The number of KM/IC‐centric and KM/IC‐relevant journals has been growing at the pace of one new journal launch per year. This demonstrates that KM/IC is not a scientific fad; instead, the discipline is progressing towards academic maturity and recognition. Practical implications The developed ranking may be used by various stakeholders, including journal editors, publishers, reviewers, researchers, new scholars, students, policymakers, university administrators, librarians and practitioners. It is a useful tool to further promote the KM/IC discipline and develop its unique identity. It is important for all KM/IC journals to become included in Thomson Reuters' Journal Citation Reports . Originality/value This is the most up‐to‐date ranking of KM/IC journals.
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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.059 | 0.066 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".