{"title":"再论大科技对人工智能研究的影响:从隶属关系看创意归属的记忆分析","authors":"Stanisław Giziński , Paulina Kaczyńska , Hubert Ruczyński , Emilia Wiśnios , Bartosz Pieliński , Przemysław Biecek , Julian Sienkiewicz","doi":"10.1016/j.joi.2024.101572","DOIUrl":null,"url":null,"abstract":"<div><p>There exists a growing discourse around the domination of Big Tech on the landscape of artificial intelligence (AI) research, yet our comprehension of this phenomenon remains cursory. This paper aims to broaden and deepen our understanding of Big Tech's reach and power within AI research. It highlights the dominance not merely in terms of sheer publication volume but rather in the propagation of new ideas or <em>memes</em>. Current studies often oversimplify the concept of influence to the share of affiliations in academic papers, typically sourced from limited databases such as arXiv or specific academic conferences.</p><p>The main goal of this paper is to unravel the specific nuances of such influence, determining which AI ideas are predominantly driven by Big Tech entities. By employing network and memetic analysis on AI-oriented paper abstracts and their citation network, we are able to grasp a deeper insight into this phenomenon. By utilizing two databases: <em>OpenAlex</em> and <em>S2ORC</em>, we are able to perform such analysis on a much bigger scale than previous attempts.</p><p>Our findings suggest that while Big Tech-affiliated papers are disproportionately more cited in some areas, the most cited papers are those affiliated with both Big Tech and Academia. Focusing on the most contagious memes, their attribution to specific affiliation groups (Big Tech, Academia, mixed affiliation) seems equally distributed between those three groups. This suggests that the notion of Big Tech domination over AI research is oversimplified in the discourse.</p></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"18 4","pages":"Article 101572"},"PeriodicalIF":3.4000,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1751157724000841/pdfft?md5=1da519ec197e0affad1838ca39a68f58&pid=1-s2.0-S1751157724000841-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Big Tech influence over AI research revisited: Memetic analysis of attribution of ideas to affiliation\",\"authors\":\"Stanisław Giziński , Paulina Kaczyńska , Hubert Ruczyński , Emilia Wiśnios , Bartosz Pieliński , Przemysław Biecek , Julian Sienkiewicz\",\"doi\":\"10.1016/j.joi.2024.101572\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>There exists a growing discourse around the domination of Big Tech on the landscape of artificial intelligence (AI) research, yet our comprehension of this phenomenon remains cursory. This paper aims to broaden and deepen our understanding of Big Tech's reach and power within AI research. It highlights the dominance not merely in terms of sheer publication volume but rather in the propagation of new ideas or <em>memes</em>. Current studies often oversimplify the concept of influence to the share of affiliations in academic papers, typically sourced from limited databases such as arXiv or specific academic conferences.</p><p>The main goal of this paper is to unravel the specific nuances of such influence, determining which AI ideas are predominantly driven by Big Tech entities. By employing network and memetic analysis on AI-oriented paper abstracts and their citation network, we are able to grasp a deeper insight into this phenomenon. By utilizing two databases: <em>OpenAlex</em> and <em>S2ORC</em>, we are able to perform such analysis on a much bigger scale than previous attempts.</p><p>Our findings suggest that while Big Tech-affiliated papers are disproportionately more cited in some areas, the most cited papers are those affiliated with both Big Tech and Academia. Focusing on the most contagious memes, their attribution to specific affiliation groups (Big Tech, Academia, mixed affiliation) seems equally distributed between those three groups. This suggests that the notion of Big Tech domination over AI research is oversimplified in the discourse.</p></div>\",\"PeriodicalId\":48662,\"journal\":{\"name\":\"Journal of Informetrics\",\"volume\":\"18 4\",\"pages\":\"Article 101572\"},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2024-08-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S1751157724000841/pdfft?md5=1da519ec197e0affad1838ca39a68f58&pid=1-s2.0-S1751157724000841-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Informetrics\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1751157724000841\",\"RegionNum\":2,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Informetrics","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1751157724000841","RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Big Tech influence over AI research revisited: Memetic analysis of attribution of ideas to affiliation
There exists a growing discourse around the domination of Big Tech on the landscape of artificial intelligence (AI) research, yet our comprehension of this phenomenon remains cursory. This paper aims to broaden and deepen our understanding of Big Tech's reach and power within AI research. It highlights the dominance not merely in terms of sheer publication volume but rather in the propagation of new ideas or memes. Current studies often oversimplify the concept of influence to the share of affiliations in academic papers, typically sourced from limited databases such as arXiv or specific academic conferences.
The main goal of this paper is to unravel the specific nuances of such influence, determining which AI ideas are predominantly driven by Big Tech entities. By employing network and memetic analysis on AI-oriented paper abstracts and their citation network, we are able to grasp a deeper insight into this phenomenon. By utilizing two databases: OpenAlex and S2ORC, we are able to perform such analysis on a much bigger scale than previous attempts.
Our findings suggest that while Big Tech-affiliated papers are disproportionately more cited in some areas, the most cited papers are those affiliated with both Big Tech and Academia. Focusing on the most contagious memes, their attribution to specific affiliation groups (Big Tech, Academia, mixed affiliation) seems equally distributed between those three groups. This suggests that the notion of Big Tech domination over AI research is oversimplified in the discourse.
期刊介绍:
Journal of Informetrics (JOI) publishes rigorous high-quality research on quantitative aspects of information science. The main focus of the journal is on topics in bibliometrics, scientometrics, webometrics, patentometrics, altmetrics and research evaluation. Contributions studying informetric problems using methods from other quantitative fields, such as mathematics, statistics, computer science, economics and econometrics, and network science, are especially encouraged. JOI publishes both theoretical and empirical work. In general, case studies, for instance a bibliometric analysis focusing on a specific research field or a specific country, are not considered suitable for publication in JOI, unless they contain innovative methodological elements.