The intellectual core and impact of the knowledge management academic discipline
Bibliographic record
Abstract
Purpose The purpose of this paper is two‐fold: to explore the intellectual core of the knowledge management (KM) academic discipline in order to test whether it exhibits signs of a reference discipline; and to analyze the theoretical and practical impact of the discipline. Design/methodology/approach The most influential articles published in the Journal of Knowledge Management were selected and their cited and citing works were scientometrically analysed. Findings The KM discipline: builds its knowledge primarily upon research reports published in the English language; successfully disseminates its knowledge in both English and non‐English publications; does not exhibit a problematic self‐citation behavior; uses books and practitioner journals in the development of KM theory; converts experiential knowledge into academic knowledge; is not yet a reference discipline, but is progressing well towards becoming one; exerts a somewhat limited direct impact on practice; and is not a scientific fad. Practical implications KM researchers need to become aware of and use knowledge published in non‐English outlets. Given the status of KM as an applied discipline, it is critical that researchers continue utilizing non‐peer reviewed sources in their scholarly work. KM researchers should promote the dissemination of KM knowledge beyond the disciplinary boundaries. The issue whether KM should strive towards becoming a reference discipline should be debated further. Originality/value This study analyzes the KM field from the reference discipline perspective.
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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 | 0.019 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".