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
Research in the KM field has been a point of attraction for innovation and sustainability. The need for research and the effort by the researchers are important to be analysed to know the overall status of contributions and contributors. The purpose of this paper is to identify trends in Knowledge management research and forecast future trends through bibliometric analysis. The study also aims to identify the highest contribution of articles by the authors, the institutions, the journals, and the countries. Microsoft Excel and VOSViewer software were used for the analysis of the data extracted from the Scopus database for the period 2003–2022. It found Bontis. N. of Canada stood out as the highest contributing author in KM research; the Hong Kong Polytechnic University of China proved to be the top contributing institution in the field; the Journal of Knowledge Management ranks first amongst the most contributing journals in the field; and the United States was the highest contributing country. Furthermore, the study found four clusters based on the co-occurrence of keywords. “Artificial Intelligence,” “Big Data,” and “knowledge hiding” are the budding areas in the field.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.005 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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