Co-authorship Networks of Iranian Researchers' Publications on the Field of Management during a Half-Century (1969-2018)
Bibliographic record
Abstract
As one of the main bibliometric concepts, co-authorship has been thoughtfully considered in recent years. Despite many bibliometric studies on the co-authorship in different scientific fields and worldwide countries/regions, Iranian researchers' collaboration in the management field has not been studied. This study aimed to investigate the co-authorship networks in the management papers contributed by Iranian researchers indexed in the Web of Science (WoS) during the recent half-century (years, 1969-2018). Bibliometric data on 5414 papers were extracted from WoS and analyzed in Excel, UCINET, and VOSviewer to measure bibliometric indicators, the map needed co-authorship networks, and depict time-based maps and keyword clustering. Findings showed that co-authored papers increased from two items in 1973 to 721 items in 2018. Expert Systems with Applications, African Journal of Business Management, and International Journal of Production Research were ranked first to third in co-authored papers. Top 20 authors published about 17% of papers (946). Islamic Azad University, University of Tehran, and the Amirkabir University of Technology ranked first to third. Most co-authorship frequencies were made from 2012 to 2014. The first to third ranks of collaborating countries were the USA, Canada, and England. Six main keyword clusters were formed, including main topics in the field. In conclusion, Iranian researchers increasingly co-authored in management, especially during the last decade, and published in various journals that some top ones are prestigious journals. However, some gaps need to be bridged by the low contribution of research institutes and universities countrywide and the limited number of authors with high productivity and low collaboration with neighbor countries and influential universities worldwide. https://dorl.net/dor/ 20.1001.1.20088302.2022.20.1.19.2
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.018 | 0.020 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".