Citation classics published in knowledge management journals. Part III: author survey
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose This paper is the third part of a series of works investigating the top 100 knowledge management (KM) citation classic articles. The purpose of this paper is to understand why KM citation classics are well-cited. Design/methodology/approach The results of a survey of 58 KM citation classic authors were reported as descriptive statistics and subjected to content analysis. Findings An archetype of a KM citation classic author was constructed including demographics, personal characteristics, motivation and work preferences. There is a need for developing novel ideas in KM research. Timeliness of a publication is directly linked to its future impact. Editors should involve citation classics authors as reviewers, and KM researchers should improve their citation practices. Serendipity played a very important role in early KM research, especially from the perspective of discovering new and interesting phenomena. Research limitations/implications Whereas the importance of serendipity is not questioned, future KM researchers should rely more on a formal, meticulous and well-planned research approach rather than on the hope of making a discovery by accident or luck. KM citation classics authors relied on serendipity to form the foundation of the discipline, but extending their work requires formal and structured inquiries. Practical implications Many authors conducted research to solve a problem to serve the needs of both practice and academia, rather than being overly theoretical. Originality/value Because KM researchers can no longer rely on past bibliometric theories, this paper helps understand why specific articles are highly cited and recommends how to conduct and develop future KM research that has impact.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it