The participation of International Authors in Journals indexed in Islamic World Science Citation Database (ISC)
Bibliographic record
Abstract
Purpose: This study investigates and analyzes the extent of international scientific cooperation among researchers publishing in Iranian journals indexed in the ISC (Islamic World Science Citation Database) between 2020 and 2022.Methodology: This applied scientometric study examines all scientific articles with international co-authors published in Iranian journals indexed in the ISC database from 2020 to 2022. The data were analyzed using SPSS software, employing descriptive statistics to uncover patterns of international collaboration. Findings: A total of 195,377 articles from Iranian publications were indexed in the ISC database during the study period. Of these, 5,934 articles involved international co-authorship, representing 3% of the total articles each year. The distribution of these co-authored articles spanned 115 countries, with the United States, Canada, Australia, and Germany as the leading international partners. The analysis also revealed that the geographical reach of collaboration was broader in 2020 compared to 2022. The primary fields of international collaboration were medicine (22.19%), biochemistry, genetics, and biomolecular sciences (11.24%), engineering (11.1%), and agricultural and biological sciences (7.33%). Regionally, Europe (35.65%) was the most frequent partner, followed by Asia (30.43%), Africa (20.88%), the Americas (11.30%), and Oceania (1.74%). Conclusion: Developed countries, particularly the United States, have played a pivotal role in Iran's international scientific collaborations. To enhance global engagement and scientific development, Iran must continue to foster international partnerships and implement policies that support these collaborations.Value: The study underscores that developed countries, particularly the United States, are key partners in Iran's international scientific collaborations. To enhance global engagement and scientific development, Iran must implement policies that foster stronger international research partnerships and address barriers such as language and funding limitations.
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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 | 0.010 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.027 | 0.048 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".