“思考、判断、注意、感觉”:约翰·w·杜克反对推理知识的机械化

Alexander Campolo
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摘要

在过去的半个世纪里,一系列关于推理的统计技术和思想在科学上取得了显著的成功。5%水平的重要性标志着在广泛的领域中科学知识的一个明确而独特的标准。然而,最近,由于对意义的不懈追求产生了一系列众所周知的科学滥用,这一公约陷入了争议。本文不是在这些争论中表明立场,而是分析它们背后的认识论价值的历史。它关注的是早期对滥用统计测试的批评者:约翰·w·杜克(John W. Tukey)。在20世纪中叶与行为科学家交谈时,Tukey坚持认为,将推理简化为一套普遍规则或机械程序来消除不确定性是一种注定要失败的追求。科学家需要接受数据分析中个人判断和决定的不可约性,即使他们冒着被指责为主观主义或随意性的风险。对于Tukey来说,科学共识的执行,甚至客观性的价值,都必须屈服于经验判断和个人良心的伦理。他对科学史的比较理解,为经验主义在较年轻的科学中保留了一个特殊的位置,从而为这些价值观提供了信息。重建Tukey的工作为战后科学的定量、形式客观性提供了另一种视角,也为当今科学提供了另一种视角,在当今,大数据和机器学习为统计推断和科学专业知识提出了棘手的新问题。
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“Thinking, Judging, Noticing, Feeling”: John W. Tukey against the Mechanization of Inferential Knowledge
During the past half-century, a set of statistical techniques and ideas about inference have experienced a remarkable scientific success. Significance at the 5 percent level has come to mark a clear and distinct criterion for scientific knowledge in a wide range of fields. Recently, however, this convention has been embroiled in controversy, as the relentless pursuit of significance has produced a range of well-known scientific abuses. Instead of staking out a position in these debates, this article analyzes the history of epistemological values underlying them. It focuses on an earlier critic of the misuse of statistical tests: John W. Tukey. Speaking to behavioral scientists in the middle of the twentieth century, Tukey insisted that reducing inference to a set of universal rules or mechanical procedures to eliminate uncertainty was a pursuit doomed to failure. Scientists needed to accept the irreducibility of individual judgments and decisions in data analysis, even when they risked charges of subjectivism or arbitrariness. For Tukey, the enforcement of scientific consensus and even the value of objectivity must yield to empirical judgments and an ethic of individual conscience. These values were informed by his comparative understanding of the history of science, which reserved a special place for empiricism in younger sciences. Reconstructing Tukey’s work offers an alternative perspective on the quantitative, formal objectivity of the postwar sciences as well as the present, where big data and machine learning have raised thorny new problems for statistical inference and scientific expertise.
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