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The evolution of scientific practice from 17th-century “Little Science” to contemporary “Big Science” has fundamentally reconfigured mechanisms of scholarly attribution and institutional evaluation. Addressing a key gap in the existing literature, this study compiles and systematizes rare, long-term empirical data on authorship patterns spanning four centuries (17th to 21st century), presented across six reference tables. We trace the acceleration of collaborative research and hyperauthorship—the proliferation of publications featuring hundreds or thousands of co-authors—driven by capital-intensive research infrastructure, interdisciplinary mega-collaborations, and pandemic-era global clinical networks.We examine the resulting ethics crisis, including the decoupling of writing from authorship, the erosion of ICMJE responsibility criteria, the surge in hyperproductive authors, and questionable practices such as multiaffiliation gaming and paid affiliations. Furthermore, using empirical findings from recent global university evaluation models, we evaluate four bibliometric counting schemes: Whole Counting, First-Author Counting, Fractional Counting, and Harmonic Counting, an analysis of which shows that traditional Whole Counting causes severe “counting inflation,” distorting university rankings in favor of network volume over substantive intellectual contribution. To mitigate these distortions, this study synthesizes and builds upon the proposals of prior literature to present a comprehensive policy concept for reforming academic evaluation systems and ranking agencies. This synthesized framework brings together mechanisms well-established across recent studies: transitioning to robust counting models (fractional and harmonic), constraining attribution to primary affiliations, implementing flexible field-specific thresholds, and integrating CRediT contribution profiles. Furthermore, the concept systematizes a four-tiered research integrity risk framework (RI²), measuring institutional exposure to delisted journals, article retractions, self-citation manipulations, and exposure to invalidated data.
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Владимир Михайлович Московкин (2026) studied this question.
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