U.S. Copyright Termination Notices 1977–2020: Introducing New Datasets

IF 1.2 2区 社会学 Q1 LAW Journal of Empirical Legal Studies Pub Date : 2022-02-27 DOI:10.1111/jels.12310
Joshua Yuvaraj, Rebecca Giblin, Daniel Russo-Batterham, Genevieve Grant
{"title":"U.S. Copyright Termination Notices 1977–2020: Introducing New Datasets","authors":"Joshua Yuvaraj,&nbsp;Rebecca Giblin,&nbsp;Daniel Russo-Batterham,&nbsp;Genevieve Grant","doi":"10.1111/jels.12310","DOIUrl":null,"url":null,"abstract":"<p>Copyright termination laws in the United States allow creators to end their copyright assignments and licences after various time periods and regain their rights. These laws are designed to protect authors and their heirs by giving them a second opportunity to profit from their works, where they might have assigned them initially for relatively little. Similar laws are in force and being recommended for implementation around the world. However, there is little data on how these laws are being used. Such data is vital because it provides insights into the pros and cons of different systems. We fill this gap by providing the first large-scale study of copyright termination notice records from the U.S. Copyright Office. Utilising data scraping and manipulation techniques in the Python programming language, we have created two brand new datasets for scholars, copyright experts, creators, publishers, and other industry stakeholders to examine. In our accompanying paper, we document some preliminary trends from the data and how it might be used for further analysis.</p>","PeriodicalId":47187,"journal":{"name":"Journal of Empirical Legal Studies","volume":"19 1","pages":"250-292"},"PeriodicalIF":1.2000,"publicationDate":"2022-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jels.12310","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Empirical Legal Studies","FirstCategoryId":"90","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/jels.12310","RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"LAW","Score":null,"Total":0}
引用次数: 0

Abstract

Copyright termination laws in the United States allow creators to end their copyright assignments and licences after various time periods and regain their rights. These laws are designed to protect authors and their heirs by giving them a second opportunity to profit from their works, where they might have assigned them initially for relatively little. Similar laws are in force and being recommended for implementation around the world. However, there is little data on how these laws are being used. Such data is vital because it provides insights into the pros and cons of different systems. We fill this gap by providing the first large-scale study of copyright termination notice records from the U.S. Copyright Office. Utilising data scraping and manipulation techniques in the Python programming language, we have created two brand new datasets for scholars, copyright experts, creators, publishers, and other industry stakeholders to examine. In our accompanying paper, we document some preliminary trends from the data and how it might be used for further analysis.

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
美国版权终止声明1977-2020:引入新的数据集
美国的版权终止法允许创作者在不同时期后终止其版权转让和许可,并重新获得他们的权利。这些法律旨在保护作者和他们的继承人,给他们第二次机会从他们的作品中获利,而他们最初可能会以相对较低的价格转让这些作品。类似的法律正在生效,并被建议在世界各地实施。然而,关于这些法律如何被使用的数据很少。这些数据至关重要,因为它提供了对不同系统优缺点的洞察。我们通过提供美国版权局版权终止通知记录的首次大规模研究来填补这一空白。利用Python编程语言中的数据抓取和操作技术,我们创建了两个全新的数据集,供学者、版权专家、创作者、出版商和其他行业利益相关者检查。在我们的论文中,我们从数据中记录了一些初步趋势,以及如何将其用于进一步分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
2.30
自引率
11.80%
发文量
34
期刊最新文献
Issue Information Market versus policy responses to novel occupational risks Network analysis of lawyer referral markets: Evidence from Indiana Emotional bargaining after litigation: An experimental study of the Coase theorem Automating Abercrombie: Machine-learning trademark distinctiveness
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1