International scientific collaboration among Iranian researchers during 1998‐2007
Bibliographic record
Abstract
Purpose The paper aims to investigate the rate of Iranian researchers collaboration with their colleagues in other countries in science citation index (SCI). In addition, it seeks to investigate the visibility of publications by Iranian researchers, and particularly the visibility of papers resulting from international collaboration. Design/methodology/approach The paper employs the survey research method to answer research questions. Any publication recorded in the SCI database from 1998 to 2007 with at least one Iranian author was recognized and transferred to a database in Excel. The total records were 33,813. This number mostly includes articles, letters, notes, and reviews. Findings The results showed that Iranian researchers have had scientific collaboration with 115 countries, and that their numbers have increased between 1998 and 2007. The results also showed that the number of domestic articles per year was 2‐3.5 times more than international ones. Investigating international collaboration in different subject areas revealed that geosciences had the biggest number of publications co‐authored internationally. Iran's main partners were the USA, Canada, and UK, respectively. European researchers were the main counterparts of Iranian researchers. In addition, Iranian researchers had mostly co‐published with their colleagues in advanced countries. Among Iranian universities and research institutions, the University of Tehran had the highest collaboration at the international level. The results revealed that the average number of citations received by international co‐authored publications was more than those received by domestic co‐authored publications. Originality/value The paper shows the situation of international collaboration among Iranian researchers and the impact of publications resulting from international collaboration.
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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 | 0.011 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".