发表机构
National Centre for Nuclear Research（国家核研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究借鉴蠕变损伤力学与生态韧性理论，提出科学表面繁荣下存在缓慢累积的隐性损伤，可能引发突发崩溃，并给出研究议程以识别临界点。
AI 中文摘要
从最显眼的量化指标来看——研究人员、论文、期刊和机构的数量——科学从未如此健康。然而，一组表面之下的指标却指向相反方向：普通论文和专利的颠覆性正在下降，最大领域中的进展放缓，复制研究稀少且往往不成功。激励体系即使在无人真正作弊的情况下也可能选择糟糕的方法。普通社会对科学家及其工作表现出一种好奇的混合态度：既钦佩又缺乏信任。我们认为，一个看似健康的表面可以与表面之下损伤的积累共存，并掩盖这种积累，这些损伤源于缓慢作用的内部和外部压力，削弱了科学的社会地位。这一过程最好不被理解为急性冲击，而是一场缓慢蔓延的危机。尽管损伤增长缓慢，但它可能导致研究作为社会机构的突然崩溃。为使这一诊断具有可操作性而非仅仅令人担忧，我们引入了两个成熟研究领域的概念和数学工具，这两个领域已经研究这种缓慢退化和突然崩溃过程：蠕变、疲劳和腐蚀的连续介质损伤力学，以及韧性、稳态转换和临界转变的生态学。我们并不声称要解决科学是在压力下仅仅保持稳健还是正接近临界点的问题；现有数据过于零散。这仍然是一个重要问题，我们提出了能够回答它的方案：寻找可测量的早期预警指标，使用耦合信任-激励-资助反馈系统的基于智能体的模型，理解对抗性攻击，以及设计韧性建设和恢复干预措施。结果是一个研究议程，以及一个论证，说明为什么科学的健康状况不能从其自身的成功报告中读出。
英文摘要
By its most visible quantitative metrics: numbers of researchers, papers, journals, and institutions, science has never looked healthier. Yet a set of subsurface indicators points the other way: the disruptiveness of the average paper and patent is falling, progress slows in the largest fields, replication is rare and often unsuccessful. Incentive systems can select for poor methods even when no one is actually cheating. Lay society shows a curious mixture of admiration and lack of trust for scientists and their work. We argue that a healthy-looking surface can coexist with, and mask, an accumulation of below-the-surface damage due to slow-acting, internally and externally driven pressures, weakening the social position of science. This process is best understood not as an acute shock but as a creeping crisis. And while the damage grows slowly, it may result in a sudden collapse of the research as social institution. To make this diagnosis tractable rather than merely alarming, we import the conceptual and mathematical apparatus of two mature research fields that already study such slow degradation and sudden collapse processes: the continuum damage mechanics of creep, fatigue, and corrosion, and the ecology of resilience, regime shifts, and critical transitions. We do not claim to resolve whether science is merely robust under strain or is approaching a tipping point; the available data are too fragmentary. This remains a question of importance and we propose what would answer it: finding measurable early-warning indicators, use of agent-based models of the coupled trust-incentive-funding feedback system, understanding adversarial attacks, and the design of resilience building and recovery interventions. The result is a research agenda, and an argument for why the health of science cannot be read off its own success reports.
Comments2 figures
