Scientometric Analysis of the International Journal of Pharmaceutical Investigation Reflected as Publish or Perish
Bibliographic record
Abstract
Objectives The present study provides a scientometric evaluation of the International Journal of Pharmaceutical Investigation (IJPI) for the period 2011 to 2025, using data extracted through the Publish or Perish software based on Google Scholar records. Materials and Methods The analysis aims to assess Publication growth, citation performance, authorship patterns, institutional productivity, and international collaboration. Results A total of 971 documents were published during the study period, showing a steady annual growth rate of 7.66%. The journal achieved an average of 11.64 citations per paper, indicating moderate scholarly influence. Authorship analysis revealed 3,059 contributing authors with an average of 4.07 co-authors per paper, reflecting a strong culture of collaborative research. India emerged of 4.07 co-authors per paper, reflecting a strong culture of collaborative research. India emerged as the most productive country with 391 publications and 7,677 citations, followed by Iran, Malaysia, and Saudi Arabia. Dr. A.P.J. Abdhul Kalam Technical University and Jamia Hamdard were identified as the most prolific and impactful institutions. The VOSviewer visualisations revealed multi-cluster collaboration networks among authors, organisations, and countries. Conclusion The study concludes that IJPI has evolved into a dynamic and steadily growing journal, contributing significantly to pharmaceutical research through sustained publication activity, collaboration and academic visibility.
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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 | 0.021 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.083 | 0.123 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".