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Foundation models (e.g.
The industrial organization of congress; or, why legislatures, like firms, are not organized as markets
Barry R. Weingast and William J. Marshall · 1988
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The Economics of Hubs: The Case of Monopoly
Ken Hendricks, Michele Piccione, and Guofu Tan · 1995
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General purpose technologies ‘engines of growth’?
Timothy F. Bresnahan and M. Trajtenberg · 1995
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Organisations and institutions: perspectives in economics and sociology
Michael Rowlinson · 1997
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Hubs, authorities, and communities
Jon M. Kleinberg · 1999
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The new political sociology of science: Institutions, networks, and power
Scott Frickel and Kelly Moore · 2006
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Institutions and norms in institutional economics and sociology
David Dequech · 2006
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Extremism propagation in social networks with hubs
Daniel W Franks, Jason Noble, Peter Kaufmann, and Sigrid Stagl · 2008
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ImageNet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Skills, tasks and technologies: Implications for employment and earnings
Daron Acemoglu and David Autor · 2010
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Empirical industrial organization: A progress report
Liran Einav and Jonathan Levin · 2010
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Network hubs in the human brain
Martijn P Van den Heuvel and Olaf Sporns · 2013
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Sociology of culture and cultural practices: The transformative power of institutions
Laurent Fleury · 2014
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Devising effective policies for bug-bounty platforms and security vulnerability discovery
Mingyi Zhao, Aron Laszka, and Jens Grossklags · 2017
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What can machine learning do? workforce implications
Erik Brynjolfsson and Tom Mitchell · 2017
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Timnit Gebru, Jamie Morgenstern, Briana Vecchione, Jennifer Wortman Vaughan, Hanna Wallach, Hal Daumé Ill, and Kate Crawford · 2018
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Data statements for natural language processing: Toward mitigating system bias and enabling better science
Emily M Bender and Batya Friedman · 2018
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Model cards for model reporting
Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru · 2018
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The race between man and machine: Implications of technology for growth, factor shares, and employment
Daron Acemoglu and Pascual Restrepo · 2018
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Quantifying the carbon emissions of machine learning
Alexandre Lacoste, Alexandra Luccioni, Victor Schmidt, and Thomas Dandres · 2019
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Energy and policy considerations for deep learning in NLP
Emma Strubell, Ananya Ganesh, and Andrew McCallum · 2019
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Actionable auditing: Investigating the impact of publicly naming biased performance results of commercial ai products
Inioluwa Deborah Raji and Joy Buolamwini · 2019
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Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
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Generative pretraining from pixels
Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, and Ilya Sutskever · 2020
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Meet gpt-3. it has learned to code (and blog and argue)
NYT · 2020
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Donald Martin Jr, Vinodkumar Prabhakaran, Jill Kuhlberg, Andrew Smart, and William S Isaac · 2020
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Towards the systematic reporting of the energy and carbon footprints of machine learning
Peter Henderson, Jieru Hu, Joshua Romoff, Emma Brunskill, Dan Jurafsky, and Joelle Pineau · 2020
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Ai and jobs: Evidence from online vacancies
Daron Acemoglu, David Autor, Jonathon Hazell, and Pascual Restrepo · 2020
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Beyond accuracy: Behavioral testing of NLP models with CheckList
Marco Tulio Ribeiro, Tongshuang Wu, Carlos Guestrin, and Sameer Singh · 2020
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Intrinsic evaluation of summarization datasets
Rishi Bommasani and Claire Cardie · 2020
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Dataset cartography: Mapping and diagnosing datasets with training dynamics
Swabha Swayamdipta, Roy Schwartz, Nicholas Lourie, Yizhong Wang, Hannaneh Hajishirzi, Noah A. Smith, and Yejin Choi · 2020
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Value-laden disciplinary shifts in machine learning
Ravit Dotan and Smitha Milli · 2020
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Utility is in the eye of the user: A critique of NLP leaderboards
Kawin Ethayarajh and Dan Jurafsky · 2020
Cited alongside, same era.
Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander Rush · 2020
Cited alongside, same era.
On the opportunities and risks of foundation models
Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S. Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, Erik Brynjolfsson, Shyamal Buch, Dallas Card, Rodrigo Castellon, Niladri Chatterji, Annie Chen, Kathleen Creel, Jared Quincy Davis, Dorottya Demszky, Chris Donahue, Moussa Doumbouya, Esin Durmus, Stefano Ermon, John Etchemendy, Kawin Ethayarajh, Li Fei-Fei, Chelsea Finn, Trevor Gale, Lauren Gillespie, Karan Goel, Noah Goodman, Shelby Grossman, Neel Guha, Tatsunori Hashimoto, Peter Henderson, John Hewitt, Daniel E. Ho, Jenny Hong, Kyle Hsu, Jing Huang, Thomas Icard, Saahil Jain, Dan Jurafsky, Pratyusha Kalluri, Siddharth Karamcheti, Geoff Keeling, Fereshte Khani, Omar Khattab, Pang Wei Koh, Mark Krass, Ranjay Krishna, Rohith Kuditipudi, Ananya Kumar, Faisal Ladhak, Mina Lee, Tony Lee, Jure Leskovec, Isabelle Levent, Xiang Lisa Li, Xuechen Li, Tengyu Ma, Ali Malik, Christopher D. Manning, Suvir Mirchandani, Eric Mitchell, Zanele Munyikwa, Suraj Nair, Avanika Narayan, Deepak Narayanan, Ben Newman, Allen Nie, Juan Carlos Niebles, Hamed Nilforoshan, Julian Nyarko, Giray Ogut, Laurel Orr, Isabel Papadimitriou, Joon Sung Park, Chris Piech, Eva Portelance, Christopher Potts, Aditi Raghunathan, Rob Reich, Hongyu Ren, Frieda Rong, Yusuf Roohani, Camilo Ruiz, Jack Ryan, Christopher Ré, Dorsa Sadigh, Shiori Sagawa, Keshav Santhanam, Andy Shih, Krishnan Srinivasan, Alex Tamkin, Rohan Taori, Armin W. Thomas, Florian Tramèr, Rose E. Wang, William Wang, Bohan Wu, Jiajun Wu, Yuhuai Wu, Sang Michael Xie, Michihiro Yasunaga, Jiaxuan You, Matei Zaharia, Michael Zhang, Tianyi Zhang, Xikun Zhang, Yuhui Zhang, Lucia Zheng, Kaitlyn Zhou, and Percy Liang · 2021
Language models generalize beyond natural proteins
Robert Verkuil, Ori Kabeli, Yilun Du, Basile I. M. Wicky, Lukas F. Milles, Justas Dauparas, David Baker, Sergey Ovchinnikov, Tom Sercu, and Alexander Rives · 2022
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Huge “foundation models” are turbo-charging ai progress
Economist · 2022
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Art isn’t dead, it’s just machine-generated
Guido Appenzeller, Matt Bornstein, Martin Casado, and Yoko Li · 2022
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Introducing: the scale generative ai index
Jeremy Kaufmann, Max Abram, and Maggie Basta · 2022
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Emergent abilities of large language models
Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, Ed H. Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, and William Fedus · 2022
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The values encoded in machine learning research
Abeba Birhane, Pratyusha Kalluri, Dallas Card, William Agnew, Ravit Dotan, and Michelle Bao · 2022
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Cited alongside, same era.
Zero-shot text-to-image generation
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever · 2021
Cited alongside, same era.
Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde, Jared Kaplan, Harrison Edwards, Yura Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott Gray, Nick Ryder, Mikhail Pavlov, Alethea Power, Lukasz Kaiser, Mohammad Bavarian, Clemens Winter, Philippe Tillet, Felipe Petroski Such, David W. Cummings, Matthias Plappert, Fotios Chantzis, Elizabeth Barnes, Ariel Herbert-Voss, William H. Guss, Alex Nichol, Igor Babuschkin, S. Arun Balaji, Shantanu Jain, Andrew Carr, Jan Leike, Joshua Achiam, Vedant Misra, Evan Morikawa, Alec Radford, Matthew M. Knight, Miles Brundage, Mira Murati, Katie Mayer, Peter Welinder, Bob McGrew, Dario Amodei, Sam McCandlish, Ilya Sutskever, and Wojciech Zaremba · 2021
Cited alongside, same era.
Highly accurate protein structure prediction with alphafold
John M. Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Zídek, Anna Potapenko, Alex Bridgland, Clemens Meyer, Simon A A Kohl, Andy Ballard, Andrew Cowie, Bernardino Romera-Paredes, Stanislav Nikolov, Rishub Jain, Jonas Adler, Trevor Back, Stig Petersen, David A. Reiman, Ellen Clancy, Michal Zielinski, Martin Steinegger, Michalina Pacholska, Tamas Berghammer, Sebastian Bodenstein, David Silver, Oriol Vinyals, Andrew W. Senior, Koray Kavukcuoglu, Pushmeet Kohli, and Demis Hassabis · 2021
Cited alongside, same era.
The big question
Nature · 2021
Cited alongside, same era.
Persistent anti-muslim bias in large language models
Abubakar Abid, Maheen Farooqi, and James Zou · 2021
Cited alongside, same era.
The Pile: An 800GB Dataset of Diverse Text for Language Modeling
Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, Shawn Presser, and Connor Leahy · 2021
Cited alongside, same era.
Multitask prompted training enables zero-shot task generalization
Victor Sanh, Albert Webson, Colin Raffel, Stephen H. Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, Manan Dey, M Saiful Bari, Canwen Xu, Urmish Thakker, Shanya Sharma Sharma, Eliza Szczechla, Taewoon Kim, Gunjan Chhablani, Nihal Nayak, Debajyoti Datta, Jonathan Chang, Mike Tian-Jian Jiang, Han Wang, Matteo Manica, Sheng Shen, Zheng Xin Yong, Harshit Pandey, Rachel Bawden, Thomas Wang, Trishala Neeraj, Jos Rozen, Abheesht Sharma, Andrea Santilli, Thibault Fevry, Jason Alan Fries, Ryan Teehan, Stella Biderman, Leo Gao, Tali Bers, Thomas Wolf, and Alexander M. Rush · 2021
Cited alongside, same era.
Notes on problem formulation in machine learning
Razvan Amironesei, Emily Denton, and Alex Hanna · 2021
Cited alongside, same era.
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LAION-5b: An open large-scale dataset for training next generation image-text models
Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade W Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, Patrick Schramowski, Srivatsa R Kundurthy, Katherine Crowson, Ludwig Schmidt, Robert Kaczmarczyk, and Jenia Jitsev · 2022
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Whose language counts as high quality? measuring language ideologies in text data selection
Suchin Gururangan, Dallas Card, Sarah K. Drier, Emily Kalah Gade, Leroy Z. Wang, Zeyu Wang, Luke Zettlemoyer, and Noah A. Smith · 2022
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The time is now to develop community norms for the release of foundation models, 2022b
Percy Liang, Rishi Bommasani, Kathleen A. Creel, and Rob Reich · 2022
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Who audits the auditors? recommendations from a field scan of the algorithmic auditing ecosystem
Sasha Costanza-Chock, Inioluwa Deborah Raji, and Joy Buolamwini · 2022
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Taxonomy of risks posed by language models
Laura Weidinger, Jonathan Uesato, Maribeth Rauh, Conor Griffin, Po-Sen Huang, John Mellor, Amelia Glaese, Myra Cheng, Borja Balle, Atoosa Kasirzadeh, Courtney Biles, Sasha Brown, Zac Kenton, Will Hawkins, Tom Stepleton, Abeba Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Iason Gabriel · 2022
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The Work of the Future: Building Better Jobs in an Age of Intelligent Machines
D.H. Autor, D.A. Mindell, E. Reynolds, and R.M. Solow · 2022
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Picking on the same person: Does algorithmic monoculture lead to outcome homogenization?
Rishi Bommasani, Kathleen A. Creel, Ananya Kumar, Dan Jurafsky, and Percy Liang · 2022
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Red teaming language models with language models
Ethan Perez, Saffron Huang, Francis Song, Trevor Cai, Roman Ring, John Aslanides, Amelia Glaese, Nathan McAleese, and Geoffrey Irving · 2022
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Understanding dataset difficulty with 𝒱 \mathcal{V} -usable information
Kawin Ethayarajh, Yejin Choi, and Swabha Swayamdipta · 2022
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Margaret Mitchell, Alexandra Sasha Luccioni, Nathan Lambert, Marissa Gerchick, Angelina McMillan-Major, Ezinwanne Ozoani, Nazneen Rajani, Tristan Thrush, Yacine Jernite, and Douwe Kiela · 2022
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Interactive model cards: A human-centered approach to model documentation
Anamaria Crisan, Margaret Drouhard, Jesse Vig, and Nazneen Rajani · 2022
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Rishi Bommasani · 2022
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Artificial intelligence and life in 2030: the one hundred year study on artificial intelligence
Peter Stone, Rodney Brooks, Erik Brynjolfsson, Ryan Calo, Oren Etzioni, Greg Hager, Julia Hirschberg, Shivaram Kalyanakrishnan, Ece Kamar, Sarit Kraus, et al · 2022
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Why you’re about to see chatgpt in more of your apps
CNN · 2023
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Chatgpt sets record for fastest-growing user base - analyst note
Krystal Hu · 2023
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Counting carbon: A survey of factors influencing the emissions of machine learning
Alexandra Sasha Luccioni and Alex Hernández-García · 2023
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Poisoning web-scale training datasets is practical
Nicholas Carlini, Matthew Jagielski, Christopher A. Choquette-Choo, Daniel Paleka, Will Pearce, H. Anderson, A. Terzis, Kurt Thomas, and Florian Tramèr · 2023
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Improving transparency in ai language models: A holistic evaluation
Rishi Bommasani, Daniel Zhang, Tony Lee, and Percy Liang · 2023
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Advances in ai: Are we ready for a tech revolution?
Aleksander Mądry · 2023
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Foundation models: The future (still) isn’t happening fast enough
Jon Turow, Palak Goel, and Tim Porter · 2023
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Gpts are gpts: An early look at the labor market impact potential of large language models, 2023
Tyna Eloundou, Sam Manning, Pamela Mishkin, and Daniel Rock · 2023
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Experimental evidence on the productivity effects of generative artificial intelligence
Shakked Noy and Whitney Zhang · 2023
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How will language modelers like chatgpt affect occupations and industries?
Edward W. Felten, Manav Raj, and Robert C. Seamans · 2023
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Language models and cognitive automation for economic research
Anton Korinek · 2023
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The impact of ai on developer productivity: Evidence from github copilot
Sida Peng, Eirini Kalliamvakou, Peter Cihon, and Mert Demirer · 2023
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Governing the algorithmic city
Seth Lazar · 2023
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Avoiding the success trap: Toward policy for open-source software as infrastructure
Stewart Scott, Sara Ann Brackett, Trey Herr, and Maia Hamin · 2023
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