The HK Index: A Disjointness‐Driven Model for Intelligent Ranking of Scientific Impact
Ghulam Mustafa et al.
What the paper says
Accurately predicting scientific impact and ranking researchers remains a central yet complex challenge in research evaluation. Traditional metrics such as citation counts, publication totals, hybrid measures, and h‐type indices each capture limited aspects of scholarly influence, making it difficult to establish a universally accepted standard. This study proposes a novel composite index designed to enhance the robustness and fairness of researcher ranking. A dataset of 1060 neuroscience researchers comprising both awardees and non‐awardees was analysed to evaluate the ability of existing indices to identify top‐performing scientists. The five indices most strongly associated with awardees were selected and further refined using deep learning models to determine their distinctiveness and combined effectiveness. Eleven statistical models were then tested to integrate the most independent pair of indices. The H2 upper and K indices exhibited the highest disjointness value (0.97), and their harmonic mean produced the most balanced and consistent performance with an average impact score of 0.76. The resulting composite index outperformed traditional metrics, offering a more comprehensive and unbiased measure of researcher impact. This approach demonstrates a scalable and data‐driven framework for improving the accuracy of scientific evaluation and ranking systems.
1 citation
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.16 × 0.4 = 0.06 |
| M · momentum | 0.53 × 0.15 = 0.08 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.