Scientometric Analysis of Medical and Non-Medical Highly Cited Papers of Iran in Essential Science Indicator (ESI)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The aim of the present research is to study scientometric indicators (the number of articles and citations, the mean citation per paper, H-index, Y-index, and the national and international collaboration) of medical and non-medical highly cited papers of Iran in the Essential Science Indicators (ESI). The research population is all highly cited articles of Iran in ESI during 2005 to 2015. Data was retrieved from ESI and was analyzed using descriptive statistics. Findings show that Iran has achieved the 35 th global rank in terms of the number of highly cited articles. This rank encompasses % 0.1 of the highly cited medical subject areas and % 0.6 of the global portion in non-medical areas. The growth of highly cited papers in non-medical subject areas is more than medical subject ones. Y-index indicates that the role of authors in most highly cited articles in both medical and non-medical subject areas is either as the corresponding or the first author, with an inclination towards the first author. Most of Iran’s highly cited articles in the medical subject areas are based on international collaborations, but in the non-medical areas, they are based on national collaborations. The most international collaborations are with U.S, Canada and England, respectively. H-index of Iran is 141. As a whole, in quantitative and qualitative assessment, non-medical subject areas have a better status than medical subject areas. In general, it can be said that the status of scientific products and the international status of Iran is not satisfactory. Professional planning and policy should be taken into consideration by Iran .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.022 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.000 | 0.017 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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