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<p>Traditional scientometric indicators often assess researchers through isolated dimensions, yielding fragmented or biased evaluations. To address this, we propose a multi-dimensional taxonomic framework that systematically integrates Productivity, Impact, Collaboration, and Prestige into a coherent set of fusion metrics. Using a systematic literature review, we identified 57 candidate metrics, which were validated by 12 scientometrics experts via the Fuzzy Delphi method and subsequently prioritized using Fuzzy TOPSIS. This process yielded 36 novel integrated metrics that capture synergies across overlapping dimensions—such as “Collaboration–Impact–Productivity.” Results show strong expert consensus (disagreement &lt; 0.2), with the impact dimension and the Collaboration–Impact–Productivity integrated metric ranking highest, while Prestige consistently ranked lowest. The novelty of this framework lies in its comprehensive integration: the extensive breadth of 57 metrics (surpassing prior limited, scope composites), the innovative modeling of dimensional overlaps through fusion metrics (extending independent treatments in existing indices), and the unique sequence of methods—literature review, Fuzzy Delphi validation, and Fuzzy TOPSIS prioritization—applied for the first time in scientometric researcher evaluation (differentiating from domain-specific applications like journal selection). Crucially, this taxonomy not only fills critical gaps in existing evaluation systems but also provides research administrators, funding agencies, and science policymakers with a transparent, robust, and multi-faceted toolkit to move beyond simplistic indicators toward holistic, evidence-based assessment of scholarly performance at scale.</p>

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Multi-dimensional taxonomic metrics for researcher evaluation

  • 期刊论文

  • 发表日期：

    2026年1月10日

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  • Somayeh GhaviDel,
  • Nosrat Riahinia,
  • Farshid Danesh,
  • Abdolreza Noroozi Chakoli

摘要

Traditional scientometric indicators often assess researchers through isolated dimensions, yielding fragmented or biased evaluations. To address this, we propose a multi-dimensional taxonomic framework that systematically integrates Productivity, Impact, Collaboration, and Prestige into a coherent set of fusion metrics. Using a systematic literature review, we identified 57 candidate metrics, which were validated by 12 scientometrics experts via the Fuzzy Delphi method and subsequently prioritized using Fuzzy TOPSIS. This process yielded 36 novel integrated metrics that capture synergies across overlapping dimensions—such as “Collaboration–Impact–Productivity.” Results show strong expert consensus (disagreement < 0.2), with the impact dimension and the Collaboration–Impact–Productivity integrated metric ranking highest, while Prestige consistently ranked lowest. The novelty of this framework lies in its comprehensive integration: the extensive breadth of 57 metrics (surpassing prior limited, scope composites), the innovative modeling of dimensional overlaps through fusion metrics (extending independent treatments in existing indices), and the unique sequence of methods—literature review, Fuzzy Delphi validation, and Fuzzy TOPSIS prioritization—applied for the first time in scientometric researcher evaluation (differentiating from domain-specific applications like journal selection). Crucially, this taxonomy not only fills critical gaps in existing evaluation systems but also provides research administrators, funding agencies, and science policymakers with a transparent, robust, and multi-faceted toolkit to move beyond simplistic indicators toward holistic, evidence-based assessment of scholarly performance at scale.