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<p>Researcher evaluation remains a central challenge in scientometrics, where reliable, transparent, and context-sensitive methods are required for decisions related to recruitment, promotion, and awards. Traditional assessment approaches rely heavily on bibliometric indices such as the <i>h</i>-index and its variants; however, these measures often neglect publication venue quality and provide limited discriminatory capability. This study proposes an integrated framework that combines retrospective bibliometric analysis, venue-aware modeling, and Genetic Programming (GP)-based symbolic regression for interpretable researcher evaluation. A balanced dataset of 1,200 computer science authors, equally divided between awardees and non-awardees, was constructed using Google Scholar and <i>Publish or Perish</i>. First, sixty-four bibliometric indices were computed to establish a retrospective ranking baseline, where the <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(h_2\)</EquationSource> </InlineEquation> upper-index achieved the strongest performance by identifying 73% of awardees within top-ranked positions. Second, a venue-aware dataset incorporating journals, conferences, books, and patents was developed, and neural-network–estimated venue contributions indicated journals as the dominant factor (84%). Finally, GP-based symbolic regression was applied to evolve interpretable closed-form equations integrating venue-level features. The best GP-derived model identified 91% of awardees, outperforming both the retrospective baseline and the venue-aware linear model while maintaining interpretability. These findings demonstrate that combining venue-aware modeling with interpretable evolutionary computation provides a more accurate, transparent, and equitable framework for researcher evaluation, with practical implications for academic institutions and research policy design.</p>

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Venue-aware researcher impact assessment using genetic programming

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月2日

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  • Kainat Mubarik,
  • Ghulam Mustafa,
  • Suliman Aladhadh,
  • Muhammad Tanvir Afzal,
  • Muhammad Shaban Qabil

摘要

Researcher evaluation remains a central challenge in scientometrics, where reliable, transparent, and context-sensitive methods are required for decisions related to recruitment, promotion, and awards. Traditional assessment approaches rely heavily on bibliometric indices such as the h-index and its variants; however, these measures often neglect publication venue quality and provide limited discriminatory capability. This study proposes an integrated framework that combines retrospective bibliometric analysis, venue-aware modeling, and Genetic Programming (GP)-based symbolic regression for interpretable researcher evaluation. A balanced dataset of 1,200 computer science authors, equally divided between awardees and non-awardees, was constructed using Google Scholar and Publish or Perish. First, sixty-four bibliometric indices were computed to establish a retrospective ranking baseline, where the \(h_2\) upper-index achieved the strongest performance by identifying 73% of awardees within top-ranked positions. Second, a venue-aware dataset incorporating journals, conferences, books, and patents was developed, and neural-network–estimated venue contributions indicated journals as the dominant factor (84%). Finally, GP-based symbolic regression was applied to evolve interpretable closed-form equations integrating venue-level features. The best GP-derived model identified 91% of awardees, outperforming both the retrospective baseline and the venue-aware linear model while maintaining interpretability. These findings demonstrate that combining venue-aware modeling with interpretable evolutionary computation provides a more accurate, transparent, and equitable framework for researcher evaluation, with practical implications for academic institutions and research policy design.