Nm Index: A Percentile-Based Metric Accounting for Author Contribution, Sequence, Influential Self-Citations, Career Stage, and Field Norms for Assessing Global Scholarly Impact.

Abstract

Even though author position is taken into account by some scores, they ignore actual contributions, particularly for middle authors or for alphabetical ordering. Furthermore, position-based scores frequently contain over-rewards and redundancies, like the single-author factor being repeated across sub-scores. Citations differ by domain and year of publication as well, but the existing scores do not account for multi-domain involvement or outlier effects, such as a few papers with a lot of citations or a few with very few. Likewise, h, g, Hf, and Hm indices vary according to article domains and career stage, which disadvantages early-career academics in sectors with few citations. Lastly, while Hm and Hf assume equal author contributions, the m-index penalizes seniors. To solve these inadequacies, this work introduces the Nilmantha (Nm) index, a novel percentile-based metric designed to address these limitations through comprehensive normalization and weighting strategies that produce a percentile score showing academic significance directly comparable internationally. Alphabetical/Random (A/R), Relative Contribution (RC) with multiple/single First Authors (FAs) or Last Authors (LAs), and Explicit Contribution (EC) with multiple/single FAs/LAs are considered. In particular, it employs a harmonic model for RC, uniform weights for A/R, proportionate uniform weights for multiple FAs/LAs per RC/EC, and AHP-driven CRediT or ACI. First, self-influence and overlapping core-author influence are taken into account when adjusting citations for influential self-citations. Second, we calculate the weighted average hf (productive cited output with fractional credit), hm (productive cited output for multi-authorship), and g (highly cited works) indices, all of which are adjusted for career factor (preserving senior academics’ collective achievements) and multiple fields. We also calculate the outlieruncontrolled weighted field- and year-normalized (WFYN) total cites (overall impact), outlier-controlled WFYN citations per paper (average paper impact), and outlier-controlled WFYN citation rate (impact rate). Fictitious author profile use cases are used to validate the model, and the numerical results show that the model captures 36% more impact than traditional C-scores in multi-field contexts, provides 118% more accurate per-paper assessments through contribution weighting, and avoids the over-penalization of senior researchers while appropriately boosting early-career scholars.