Citation classics published in knowledge management journals. Part I: articles and their characteristics
Bibliographic record
Abstract
Purpose – The purpose of this study is to develop a list of citation classics published in knowledge management (KM) journals and to analyze the key attributes and characteristics of the selected articles to understand the development of the KM discipline. Design/methodology/approach – This study identifies 100 citation classics from seven KM-centric journals based on their citation impact reported by Google Scholar and analyzes their attributes. Findings – The KM discipline is at the pre-science stage because of the influence of normative studies espousing KM practice. However, KM is progressing toward normal science and academic maturity. While the discipline does not exhibit the signs of the superstar effect, scholars from the USA and UK have made the most significant impact on the development of the KM school of thought. KM scholars should be more engaged in international collaboration. Practical implications – Practitioners played a key role in the development of the KM discipline and thus there is an opportunity to develop more scientific research approaches based on critical and performative research agenda. Originality/value – The study is novel and a must read for KM scholars because it is the first to comprehensively analyze the ideas that are the origins of the KM discipline.
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 | 0.003 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.097 | 0.131 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".