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Presents Putnam’s schema I as capturing falsifiability through theory plus auxiliary statements, while schema II captures explanation of facts. Develops a machine-learning interpretation using neural networks, training parameters, and model explainability to show how deep learning can exemplify the interplay between predictive constraints and explanatory roles.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/putnams-critical-and-explanatory-tendencies-interpreted-from-a-machine-learning-perspective-paper/290665/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/putnams-critical-and-explanatory-tendencies-interpreted-from-a-machine-learning-perspective-paper/290665.png","ImageObject",300,407,{"name":92,"@type":93},"Levi","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-18","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":19},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What does schema I represent in Putnam’s framework?","Question",{"text":112,"@type":113},"Schema I pairs a theory with auxiliary statements and asks whether the prediction is true or false. It is used to connect falsifiability with the joint role of theory and auxiliaries.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does schema II differ from schema I?",{"text":117,"@type":113},"Schema II emphasizes a fact to be explained, requiring theory to be conjoined with prediction so that the target fact can be explained rather than merely assessed for falsity.",{"name":119,"@type":110,"acceptedAnswer":120},"Why does the author claim the critical and explanatory tendencies are interdependent?",{"text":121,"@type":113},"The argument is that if a scientific problem fits schema I then auxiliary statements must have explanatory power, and if it fits schema II then theory must be conjoined with prediction for facts to be explained. Together this makes the tendencies necessarily interdependent.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},290665,1789642315,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":19,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":44,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":129,"read_time":143,"preview_page_count":4},7971461740909,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","Putnam’s Critical and Explanatory Tendencies Interpreted from a Machine Learning Perspective  \nSheldon Z. Soudin  \nIntroduction  \nMaking sense of theory choice in normal and across extraordinary science is central to philosophy of science. The emergence of machine learning models has the potential to act as a wrench in the gears of current debates. In this paper, I will attempt to reconstruct the main movements that lead to and came out of Putnam’s critical and explanatory tendency distinction, argue for the biconditional necessity of the tendencies, and conceptualize that wrench through a machine learning interpretation of my claim. Some preliminary definitions and statements of assumptions are in order.  \nKuhn’s picture of normal versus extraordinary science is presented in his 1962 book “The Structure of Scientific Revolution”. In a short caricature of the distinction, normal science takes place within paradigms and extraordinary science takes place across paradigms. As such, extraordinary science entails scientific revolutions and paradigm shifts. The term“paradigm”thus becomes an important term for Kuhn’s argument; however, it remains relatively ambiguously defined. For the purposes of this paper a paradigm may be reduced to established scientific theories, symbolic generalizations, and heuristic models. As a response to Kuhn’s and Popper’s positions on the nature of good theory choice, Putnam constructs schemata to illustrate two tendencies in the consideration of scientific problems. (The ‘Corroboration’ of Theories)  \nThe critical tendency:  \n“ SCHEMA I  \nTHEORY  \nAUXILIARY STATEMENTS  \nPREDICTION – TRUE OR FALSE? ” (Putnam, 1979)  \nThe explanatory tendency:“ SCHEMA II  \nTHEORY  \n??????????  \nFACT TO BE EXPLAINED ” (Putnam, 1979)  \nPutnam endeavors to argue that Popper’s falsifiability criterion is captured by schema I, and that theories alone cannot predict anything. It is rather the conjunction of theory and auxiliary statements that make a prediction. He suggests the explanatory tendency better captures the process of theory choice that occurs in normal science.  \nMachine learning, and more specifically deep learning is a computer science and applied mathematics method that constructs neural networks, trains it on a given data set and ultimately outputs recreations/predictions that emulate the data set, upon which it was trained. The specific training methods and modes of application vary, from convolutional neural networks to recurrent  \nneural networks, from image classification to large language models (i.e. ChatGPT) . For the sake of this paper and argumentation, I assume that neural networks are scalable, that is that increasing the number of computational resources in the model’s training directly result in higher accuracy in the model’s predictions. Two additional concepts are pertinent; abstractly put, parameters are the numerical values of neural networks. As such, it is these that are learned when a computer goes through the energetically consuming process of training a large language model for example. Since these parameters are inherently non-qualitative, and extensive in their number; model explainability is understood as the process of attempting to derive meaning from learned parameters.  \nMy argument is the following: (P1) Ifa scientific problem is successfully represented by schema I, then the auxiliary statements have explanatory power. (P2) If a scientific problem is successfully represented by schema II, then theory must be conjoined with prediction for facts tobe explained. (C) Therefore, the critical and explanatory tendencies are necessarily interdependent. That is, if a scientific problem is represented by the critical tendency, then it is dependent on the explanatory tendency. And if a scientific problem is represented by the explanatory tendency, then it is dependent on the critical tendency. Throughout, I present why the nature and capacity of deep learning models is a representative examp","cbCaihJJKmMHzuF2","https://ap.wps.com/l/cbCaihJJKmMHzuF2","pdf",170743,"English","# Introduction\n# Kuhn’s Normal vs. Extraordinary Science\n# Putnam’s Critical and Explanatory Tendencies\n# Machine Learning and Model Explainability\n# Main Argument (P1, P2, C)\n# Support for P1\n## Putnam’s Corroboration Example","[{\"question\":\"What does schema I represent in Putnam’s framework?\",\"answer\":\"Schema I pairs a theory with auxiliary statements and asks whether the prediction is true or false. It is used to connect falsifiability with the joint role of theory and auxiliaries.\"},{\"question\":\"How does schema II differ from schema I?\",\"answer\":\"Schema II emphasizes a fact to be explained, requiring theory to be conjoined with prediction so that the target fact can be explained rather than merely assessed for falsity.\"},{\"question\":\"Why does the author claim the critical and explanatory tendencies are interdependent?\",\"answer\":\"The argument is that if a scientific problem fits schema I then auxiliary statements must have explanatory power, and if it fits schema II then theory must be conjoined with prediction for facts to be explained. Together this makes the tendencies necessarily interdependent.\"}]","Putnam’s Critical and Explanatory Tendencies Interpreted from a Machine Learning Perspective - Paper | PDF",23]