Mapping co-authorship network of Iranian researchers in the field of knowledge management
Bibliographic record
Abstract
Background and aim: So far, many researches have been conducted on the co-authorship study of all authors of universities and organizations as one of the most important topics in the field of scientometrics in various fields and disciplines. The aim of this study was to map the co-authorship network of Iranian researchers in the field of knowledge management in Web of Science (WoS). Material and methods: In this descriptive-survey study with a scientometric approach, the title "Knowledge Management" was used to collect data and 3015 records were retrieved. After entering the required data into Excel, the records were separated by two or more authors, and the required graphs and tables were drawn. The maps used in the study were drawn using VOSviewer. Findings: The results showed that the highest amount of Iranian researchers' scientific output was in 2017 and the lowest in 2010. Moreover, the highest outputs were in the fields of knowledge management, engineering, environmental science and computer science. Among the 1680 organizations involved in scientific outputs of Iranian researchers in the field of knowledge management, the Islamic Azad University and University of Tehran received the first and second rank in joint organizational outputs, respectively. Salehi and Ahmadi had the most collaboration on the researchers' scientific communication network. The most collaboration of Iranian authors was with authors from the USA, Canada, Britain and Australia, respectively. Conclusion: The findings indicated some ups and downs in the release process of scientific outputs in the field of knowledge management. The results showed that scholars and authors of Iranian universities had little interest in national co-authorship and had the highest cooperation with developed countries. Changing universities' incentive policies will be effective in correcting this and improving the country's ranking. Researchers can achieve this by sharing knowledge and new information tools and membership in national co-authorship networks in this 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.
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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".