LOTKA'S LAW OF SCIENTIFIC PRODUCTIVITY AND BRADFORD'S LAW OF SCATTER AMONG RESEARCHERS AT ISFAHAN UNIVERSITY OF MEDICAL SCIENCES BASED ON WEB OF SCIENCE DATABASE
Why this work is in the frame
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Bibliographic record
Abstract
• Introduction: The articles indexed in accredited citation databases essentially indicate how scientists share knowledge and promote sustainable development in each country. Therefore, according to citations to the papers of individuals, it could be possible to assess the rate of their acceptability in the scientific community. The main objective of this study was to review Lotka's law of scientific productivity and Bradford's law of scatter in scientific productions among researchers at Isfahan University of Medical Sciences (IUMS) whose articles have been cited in Web of Science (WOS) database during 1992-2008. • Methods: This was an applied study using scientometric indicators. Data was collected, sorted and analyzed in two phases and with two tools. In the first stage, data was extracted from the WOS in the form of plain text and stored on a personal computer. In the second stage, using ISI.exe, data was identified, analyzed and entered into spreadsheets in Microsoft Excel. In this research, Bradford's law of scatter, collaboration rates formula and Lotka's law of scientific production were used. • Results: The results showed that the distribution of articles by authors at IUMS followed Lotka's law, i.e., a few writers released a large portion of the scientific products. In addition, the distribution frequency of journals published by IUMS followed Bradford's law, i.e., a small number of journals published the highest number of scientific papers. Moreover, the researchers at IUMS collaborated most with authors from the United States, Canada and England. • Conclusion: The results of the present study indicated that the researchers of IUMS highly collaborate in writing their papers. Generally, collaboration rate in this university was equal to 0.967 which was relatively high. • Keywords: Bibliometrics; Medicine; Medical Informatics; Authorship; Researchers; Collaboration.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.030 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it