Comparison of Citation Patterns in Dissertations of Medical Students in Rafsanjan University of Medical Sciences ( RUMS ) During Three Time Periods Between Years 1372-1386
Bibliographic record
Abstract
Introduction: Citation analysis has a significant role in research projects. This study aimed at analyzing citations in dissertations of Rafsanjan medical university during three five-year periods. Methods: In this cross- sectional study, 519 dissertations of Rafsanjan medical students presented during years 1372 until 1386 were analyzed through a checklist. Data referring to three five- year periods (1372-76, 1377-81, 1382-86) were analyzed by Chi-square and ANOVA. Results: The comparison of mean scores for the variable (number of references,articles,books, published articles in ISI journals,and electronic references) showed that during the last period citation of the references has significantly grown (P=0.0001). ISI registered articles had been cited twice as much (14.25±21.68), while there was a slight increase in the citation of electronic references (0.32± 1.22). Mean score for number of cited references was 28.07±26.65, and 50.1% of the dissertations had observed citation standards. During 1372 to 1376 Harward citation standards had been used more (77.8%). Conclusion: The finding showed the average uses of referencing, citation standards, and Vancouver citation style have increased during the three periods, However, the increase in the use of electronic reference has not been significant.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".