Qabil, Muhammad Shaban and Mukhtiar, Hafiza Zarafshan and Mustafa, Ghulam and Afzal, Muhammad Tanvir and Díez, Isabel De la Torre and Caro Montero, Elizabeth and Garat de Marin, Mirtha Silvana UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, elizabeth.caro@uneatlantico.es, silvana.marin@uneatlantico.es (2026) Predicting Academic Award Recognition Across Disciplines Using Publication-Based Bibliometric Indices and SHAP-Driven Explainability. Information, 17 (6). p. 515. ISSN 2078-2489
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Abstract
Researcher evaluation underpins critical academic decisions, yet traditional bibliometric indicators lack predictive capability and cross-domain generalizability, while most predictive approaches offer limited interpretability and narrow domain validation. This study proposes a SHAP interpretable, multi-domain supervised learning framework for predicting academic award recognition using thirty two publication count-based bibliometric indices. A balanced dataset was constructed across four disciplines, namely Computer Science, Neuroscience, Mathematics, and Civil Engineering, comprising verified awardees from recognized professional societies and matched non-awardee researchers. Eight classifiers were evaluated under stratified five fold cross validation, assessed via accuracy, precision, recall, F1-score, and ROC AUC. The framework achieved domain-specific F1-scores of 0.70 in Computer Science, 0.73 in Neuroscience, 0.72 in Civil Engineering, and 0.78 in Mathematics, with SVM and XGBoost demonstrating the strongest cross-domain robustness across disciplines. SHAP analysis consistently identified normalized h index, h2 family, q2 index, and g index as dominant cross-domain predictors, while domain-specific indicators, including Rm and w indices in Neuroscience and P index in Civil Engineering, reflected disciplinary recognition patterns. By unifying publication-based feature engineering, multi-domain classification, and SHAP explainability within a single reproducible pipeline, this framework offers a scalable, transparent, and evidence-based tool for institutional researcher evaluation.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | award prediction; publication-based bibliometric indices; multi-domain classification; supervised learning; SHAP explainability; scientometrics |
| Subjects: | Subjects > Engineering |
| Divisions: | Europe University of Atlantic > Research > Articles and books Ibero-american International University > Research > Articles and books Ibero-american International University > Research > Articles and Books Universidad Internacional do Cuanza > Research > Articles and books |
| Depositing User: | Sr Bibliotecario |
| Date Deposited: | 17 Jul 2026 10:55 |
| Last Modified: | 17 Jul 2026 10:55 |
| URI: | https://repositorio.funiber.org/id/eprint/28810 |
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