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It engages histories of writing and computation, including algorithmic writing and electronic literature, and argues for using machine learning’s associative, predictive, and regenerative capacities in playful, critical, and contemplative goals rather than automating writing.",{"@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/climate-futures-machine-learning-from-cli-fi/290198/",{"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/climate-futures-machine-learning-from-cli-fi/290198.png","ImageObject",300,407,{"name":92,"@type":93},"Patrick","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-18","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the Climate Futures experiment about?","Question",{"text":112,"@type":113},"Climate Futures repurposes AI as a co-author of climate stories and a co-designer of climate-related images to enable reflections on living with climate change now and in the future.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does the paper position its AI use compared with product-oriented AI writing tools?",{"text":117,"@type":113},"The paper argues against automating writing and instead emphasizes interesting collaboration, using machine learning for playful, critical, and contemplative goals.",{"name":119,"@type":110,"acceptedAnswer":120},"Which machine learning systems are trained and what data is used?",{"text":121,"@type":113},"The authors trained machine learning algorithms (GPT-2 and AttnGAN) using climate-fiction novels as a dataset of cultural imaginaries of the future.","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},290198,1789640918,{"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":52,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":46,"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":31,"preview_page_count":4},549758146520,"https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470","UvA-DARE (Digital Academic Repository)  \nClimate futures: Machine learning from cli-fi  \nSánchez Querubín, N. ; Niederer, S.  \nDOI  \n10.1177/13548565221135715  \nPublication date  \n2024  \nDocument Version  \nFinal published version  \nPublished in  \nConvergence : The International Journal of Research into New Media Technologies  \nLicense  \nCC BY  \nLink to publication  \nCitation for published version (APA):  \nSánchez Querubín, N. , & Niederer, S. (2024) . Climate futures: Machine learning from cli-fi. Convergence : The International Journal of Research into New Media Technologies , 30(1), 285–303. [https://doi.org/10.1177/13548565221135715](https://doi.org/10.1177/13548565221135715)  \nGeneral rights  \nIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons) .  \nDisclaimer/Complaints regulations  \nIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: [https://uba.uva.nl/en/contact](https://uba.uva.nl/en/contact), or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible.  \nUvA-DARE is a service provided by the library of the University of Amsterdam ( [http](https://dare. uva. nl)[s](https://dare. uva. nl)[://dare. uva. nl](https://dare. uva. nl))  \nDownload date:13 Jan 2025  \nSpecial Issue: Critical Technical Practice(s) in Digital Research  \nClimate futures: Machine learning from cli-fi  \nNatalia Snchez Querub n, PhD􀀁  \nUniversity of Amsterdam, Media Studies, Amsterdam Netherlands  \nConvergence: The International Journal of Research into New Media Technologies 2024, Vol. 30(1) 285–303 © The Author(s) 2022  \nArticle reuse guidelines:  \n[sagepub.com/journals-permissions](sagepub.com/journals-permissions)  \n[DOI: 10.1177/13548565221135715](DOI: 10.1177/13548565221135715)[ ](DOI: 10.1177/13548565221135715)[journals.sagepub.com/home/con](journals.sagepub.com/home/con)  \nSabine Niederer, PhD  \nAmsterdam University of Applied Sciences, Visual Methodologies Collective, Amsterdam, Netherlands  \nAbstract  \nThis paper introduces and contextualises Climate Futures, an experiment in which AI was repurposed as a ‘co-author’ of climate stories and a co-designer of climate-related images that facilitate reflections on present and future(s) of living with climate change. It converses with histories of writing and computation, including surrealistic ‘algorithmic writing’, recombinatory poems and‘electronic literature’. At the core lies a reflection about how machine learning’s associative, predictive and regenerative capacities can be employed in playful, critical and contemplative goals. Our goal is not automating writing (as in product-oriented applications of AI). Instead, as poet Charles Hartman argues, ‘the question isn’t exactly whether a poet or a computer writes the poem, but what kinds of collaboration might be interesting’ (1996, p. 5). STS scholars critique labs as future-making sites and machine learning modelling practices and, for example, describe them also as fictions. Building on these critiques and in line with ‘critical technical practice’ (Agre, 1997), we embed our critique of ‘making the future’ in how we employ machine learning to design a tool for looking ahead and telling stories on life with climate change. This has involved engaging with climate narratives and machine learning from the critical and practical perspectives of artistic research. We trained machine learning algorithms (i.e. GPT-2 and AttnGAN) using climate fiction novels (as adataset of cultural imaginaries of t","cbCaieMbyJaA5z74","https://ap.wps.com/l/cbCaieMbyJaA5z74","pdf",1447583,"English","# Abstract\n# Keywords\n# Article overview\n## AI as co-author for climate stories\n## Machine learning capacities and critical technical practice\n## Training and generating climate-fiction stories and images\n## Tarot-like deck for climate-change conversations","[{\"question\":\"What is the Climate Futures experiment about?\",\"answer\":\"Climate Futures repurposes AI as a co-author of climate stories and a co-designer of climate-related images to enable reflections on living with climate change now and in the future.\"},{\"question\":\"How does the paper position its AI use compared with product-oriented AI writing tools?\",\"answer\":\"The paper argues against automating writing and instead emphasizes interesting collaboration, using machine learning for playful, critical, and contemplative goals.\"},{\"question\":\"Which machine learning systems are trained and what data is used?\",\"answer\":\"The authors trained machine learning algorithms (GPT-2 and AttnGAN) using climate-fiction novels as a dataset of cultural imaginaries of the future.\"}]","Climate futures - Machine learning from cli-fi | PDF"]