当AI重新定义「科研影响力」：一场关于CSRankings的反思与重塑

Core Viewpoint - The article discusses the evolution of academic ranking systems, emphasizing the shift from quantity-based metrics, such as the number of published papers, to quality-based assessments that reflect true academic impact and influence [2][12]. Group 1: Issues with Current Ranking Systems - Traditional ranking systems like USNews rely on subjective surveys, while CSRankings uses objective metrics like publication counts, leading to a competition focused on quantity rather than quality [2][3]. - The reliance on citation counts to measure academic influence has its drawbacks, as not all citations indicate significant contributions to the field [3][4]. Group 2: New Approaches to Measuring Impact - A new academic ranking system has been developed by researchers from Oregon State University and the University of California Santa Cruz, utilizing large language models (LLMs) to assess the impact of academic papers [5][7]. - The LLM analyzes top AI conference papers from 2020-2025 to identify the five most important references cited by each paper, aiming to uncover the foundational works that drive innovation in the field [7][8]. Group 3: Implementation of the New Ranking System - The new system maps the identified key references back to their authors and institutions, assigning academic influence points based on how often a paper is cited as a key reference by new research [10][12]. - This approach rewards institutions that contribute to groundbreaking discoveries and foundational research, shifting the focus from mere publication counts to genuine academic influence [12][13]. Group 4: Results and Rankings - The resulting rankings highlight institutions that have significantly impacted their fields, showcasing a more nuanced understanding of academic contributions [12][14]. - The article provides specific rankings of institutions based on their impact scores, illustrating the effectiveness of this new methodology in recognizing true academic excellence [16][21].