تحلیل سطوح همکاری علمی پژوهشگران ایرانی در پایگاه وبآوساینس : مطالعه موردی حوزه علوم اجتماعی
Bibliographic record
Abstract
The purpose of this study is to analyze the scientific cooperation network of Iranian researchers in the field of social sciences at the Web-based site from the beginning to the end of 2014. Scientometrics and network analysis indicators were used. Statistical population consists of scientific production of Iranian researchers in the Web of Science since the appearance of the first publication (with an affiliation to an Iranian university/institute) to the end of 2014 (9318 articles). The data were extracted from the Web of Science and analysed by Bibexcel, HistCite, and VOS Viewer. The findings of the research indicate that the articles of cooperation in the field of social sciences have been on a par with the following years. Based on the findings of the research, Iran's position in Middle Eastern countries is ranked third among the countries of Zionist regime and Turkey in terms of scientific production. Iran collaborated most with USA, UK, and Canada.The result showed 46.08 percent inter-institutional collaboration, 24.55 per cent intra-institutional collaboration, and 22 percent international collaboration. Overall, results indicated the average collaboration coefficient was 64%, and the trend has been upward, which indicates an increased willingness by Iranians authors for cooperative papers. The most dominated model of authorship was two and three authored articles respectively. In general, the findings of this study indicate the willingness of researchers to produce science in a collaborative way.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.011 |
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".