[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117251-en":3,"doc-seo-117251-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},117251,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Climate futures: Machine learning from cli-fi - Research and data-driven AI storytelling","This paper presents Climate Futures, an experiment repurposing AI as a co-author of climate stories and a co-designer of climate-related images. It frames the work through histories of writing and computation, including algorithmic writing and electronic literature. The core inquiry examines how machine learning’s associative, predictive, and regenerative capacities can support playful, critical, and contemplative collaboration rather than automation. It discusses critical technical practice and STS critiques of future-making labs, then reports training GPT-2 and AttnGAN on climate-fiction novels to generate a tarot-like deck and story-book for climate-change dialogue.","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, P.O. Box 19185 , 1000 GD 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:01 Aug 2026  \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","cbCaifgNVfSNrkR2","https://ap.wps.com/l/cbCaifgNVfSNrkR2","pdf",1427867,1,20,"English","en",105,"# Abstract\n## Core aim and theoretical framing\n## Critical technical practice and STS critiques\n## Method: training on climate fiction\n## Outputs: tarot-like deck and story-book","[{\"question\":\"What is the Climate Futures experiment described in the paper?\",\"answer\":\"Climate Futures repurposes AI as a co-author for climate stories and as a co-designer of climate-related images to support reflection on living with climate change.\"},{\"question\":\"How does the paper position machine learning in relation to writing and literature?\",\"answer\":\"It connects the approach to histories of writing and computation, including algorithmic writing and recombinatory/electronic literature, focusing on collaboration rather than automating authorship.\"},{\"question\":\"How were the machine learning models trained and what were the outputs?\",\"answer\":\"GPT-2 and AttnGAN were trained using climate-fiction novels as a dataset of cultural imaginaries, then prompted to generate new stories and images. The researchers edited results into a tarot-like deck and a story-book.\"}]","Climate futures: Machine learning from cli-fi - Research and data-driven AI storytelling | PDF",1785674674,50,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"climate-futures-machine-learning-from-cli-fi-research-and-data-driven-ai-storytelling","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/climate-futures-machine-learning-from-cli-fi-research-and-data-driven-ai-storytelling/117251/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the Climate Futures experiment described in the paper?","Question",{"text":75,"@type":76},"Climate Futures repurposes AI as a co-author for climate stories and as a co-designer of climate-related images to support reflection on living with climate change.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the paper position machine learning in relation to writing and literature?",{"text":80,"@type":76},"It connects the approach to histories of writing and computation, including algorithmic writing and recombinatory/electronic literature, focusing on collaboration rather than automating authorship.",{"name":82,"@type":73,"acceptedAnswer":83},"How were the machine learning models trained and what were the outputs?",{"text":84,"@type":76},"GPT-2 and AttnGAN were trained using climate-fiction novels as a dataset of cultural imaginaries, then prompted to generate new stories and images. 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