| Item type |
テクニカルレポート / Technical Report(1) |
| 公開日 |
2022-06-08 |
| タイトル |
|
|
タイトル |
Role of Machine Learning and Organizational Structure in Science |
|
言語 |
en |
| 言語 |
|
|
言語 |
eng |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
Team science |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
Machine Learning |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
Boundary Spanner |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
Collaboration |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
Artificial Intelligence |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
Interdisciplinary science |
| 資源タイプ |
|
|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_18gh |
|
資源タイプ |
technical report |
| 著者 |
Kyaw, Thu Moe
Beppu, Shotaro
Yarime, Masaru
| en |
Yarime, Masaru
The University of Tokyo
The Hong Kong University of Science and Technology
|
Search repository
Shibayama, Sotaro
|
| 著者所属 |
|
|
言語 |
en |
|
値 |
Graduate School of Public Policy, the University of Tokyo |
| 著者所属 |
|
|
言語 |
en |
|
値 |
School of Engineering, the University of Tokyo |
| 著者所属 |
|
|
言語 |
en |
|
値 |
Graduate School of Public Policy, the University of Tokyo |
| 著者所属 |
|
|
言語 |
en |
|
値 |
Division of Public Policy, the Hong Kong University of Science and Technology |
| 著者所属 |
|
|
言語 |
en |
|
値 |
Institute of Future Initiatives, the University of Tokyo |
| 著者所属 |
|
|
言語 |
en |
|
値 |
School of Economics and Management, Lund University |
| 抄録 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
The progress of science increasingly relies on machine learning (ML) and machines work alongside humans in various domains of science. This study investigates the team structure of ML-related projects and analyzes the contribution of ML to scientific knowledge production under different team structure, drawing on bibliometric analyses of 25,000 scientific publications in various disciplines. Our regression analyses suggest that (1) interdisciplinary collaboration between domain scientists and computer scientists as well as the engagement of interdisciplinary individuals who have expertise in both domain and computer sciences are common in ML-related projects; (2) the engagement of interdisciplinary individuals seem more important in achieving high impact and novel discoveries, especially when a project employs computational and domain approaches interdependently; and (3) the contribution of ML and its implication to team structure depend on the depth of ML. |
|
言語 |
en |
| 書誌情報 |
IFI Working Paper
巻 11,
発行日 2022-05
|
| 出版者 |
|
|
出版者 |
東京大学未来ビジョン研究センター |
|
言語 |
ja |
| 出版者 |
|
|
出版者 |
Institute for Future Initiatives, The University of Tokyo |
|
言語 |
en |
| 関係URI |
|
|
関連タイプ |
isIdenticalTo |
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|
識別子タイプ |
URI |
|
|
関連識別子 |
https://ifi.u-tokyo.ac.jp/en/news/10455/ |
| 関係URI |
|
|
関連タイプ |
isIdenticalTo |
|
|
識別子タイプ |
URI |
|
|
関連識別子 |
https://ifi.u-tokyo.ac.jp/en/wp-content/uploads/2022/05/WP011.pdf |
WP011.pdf (1.3MB)
