Visualization of scientific products and journals at the global level: Casting a glance at Islamic Republic of Iran
Bibliographic record
Abstract
Introduction: Scientific production in each country is indicative of its development and the scientific journals are considered one of the efficient tools for scientific communication and primary characteristics of a social system development. The present research intends to visualize the distribution of scientific journals and documents in the field of medicine indexed in Scopus database during the years 1996-2012 as well as determining Iranrs position in this field. Methods: This is an analytic descriptive study in which all published documents in the field of medicine are investigated via the output of Scopus database during the years 1996-2012. SPSS and Node XL software were used to analyze data and to draw graphs. Results: Findings showed that ten countries produced about 70 of the scientific documents in the field of medicine. Furthermore, just 86 countries had indexed journals in Scopus the majority of which (65) were published by developed countries. Findings showed that the most cited documents were published by developed countries including US, UK and Canada. Conclusion: Scientific products and journals in the field of medicine have experienced an upward trend in Scopus and this was followed by a rapid increase in recent years. There was a significant relationship between the number of documents and citations.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.015 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".