An Analysis of Local and Global Trends of the Knowledge Auditing in the Citation Analysis Database of Clarivate Analytics
Bibliographic record
Abstract
Knowledge auditing is essential for assessing knowledge management processes and adapting them with the organizational goals. On the other hand, analyzing the process of KM at local and global levels helps to identify the gaps in this field. This is an applied research which follows descriptive-analytical and scientometric methods. The statistical population of this research includes 84 ISI qualified articles of knowledge auditing in the Clarivate Analytics citation database. Data collection tools included checklists and annotation lists, and data analysis co-citation maps were performed by HisCite, VOS viewer, Pajek and Excel software. Findings showed that the publication trends of the knowledge auditing articles has fluctuated through time, and the most frequent article topics related to business finance, management, library and information science, and general and internal medicine. The number of local citations of countries, organizations, authors, journals and topics is very low compared to global citations. Findings also showed that most articles were published in English, and that the United States, England and Canada had the most publication of local and global citations. Universities of Colorado, Alabama, Waterloo and Cornell, and the Journals of Practice and Theory, Accounting Review and Auditing- had the most local and global citations respectively. Co-occurrence maps of the keywords of this area are not coherent. Moreover, among the authors, Bonner, Libby, DeZoort and Salterio composed the most frequently cited at local and global fields. Centrality networks, and close relationship among the authors were not at an acceptable level requiring better planning and policy making.
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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.129 | 0.135 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".