{"title":"Whose work matters? A tool for identifying and developing more inclusive physics textbooks","authors":"Tai Xiang, William Gray, Janice Hudgings","doi":"10.1119/5.0148649","DOIUrl":null,"url":null,"abstract":"The lack of representational diversity and role models in physics, including in our textbooks and curricular materials, is an oft-cited contributing factor to the continuing dramatic under-representation of women and people of color in physics. In this work, we develop an automated, Python-based tool for identifying the names and demographics of scientists who are mentioned in indices and chapters of physics textbooks, enabling authors, publishers, and users of physics textbooks to rapidly analyze the demographics of these texts. We quantitatively validate the automated tool using standard machine learning metrics, attaining high accuracy, precision, recall, and F1 scores. The tool is then used to demonstrate two of the many potential applications: examining whose work is mentioned in the entire collection of textbooks used in a representative four-year undergraduate physics major curriculum as well as an analysis of the demographics of scientists mentioned in a selection of ten introductory physics textbooks. Both of the sample analyses result in a similar portrait, showing that the undergraduate physics textbooks examined in this work focus overwhelmingly on work attributed to White men of European, British, and North American descent. This work points to an urgent need for the physics education community, including textbook publishers, authors, and adopters, to work together to broaden our portrayals of physics to reflect the vast diversity of scientists, both historically and contemporaneously, who are working in this field.","PeriodicalId":7589,"journal":{"name":"American Journal of Physics","volume":null,"pages":null},"PeriodicalIF":0.8000,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"American Journal of Physics","FirstCategoryId":"101","ListUrlMain":"https://doi.org/10.1119/5.0148649","RegionNum":4,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"EDUCATION, SCIENTIFIC DISCIPLINES","Score":null,"Total":0}
引用次数: 0
Abstract
The lack of representational diversity and role models in physics, including in our textbooks and curricular materials, is an oft-cited contributing factor to the continuing dramatic under-representation of women and people of color in physics. In this work, we develop an automated, Python-based tool for identifying the names and demographics of scientists who are mentioned in indices and chapters of physics textbooks, enabling authors, publishers, and users of physics textbooks to rapidly analyze the demographics of these texts. We quantitatively validate the automated tool using standard machine learning metrics, attaining high accuracy, precision, recall, and F1 scores. The tool is then used to demonstrate two of the many potential applications: examining whose work is mentioned in the entire collection of textbooks used in a representative four-year undergraduate physics major curriculum as well as an analysis of the demographics of scientists mentioned in a selection of ten introductory physics textbooks. Both of the sample analyses result in a similar portrait, showing that the undergraduate physics textbooks examined in this work focus overwhelmingly on work attributed to White men of European, British, and North American descent. This work points to an urgent need for the physics education community, including textbook publishers, authors, and adopters, to work together to broaden our portrayals of physics to reflect the vast diversity of scientists, both historically and contemporaneously, who are working in this field.
在物理学领域,包括在我们的教科书和课程材料中,缺乏代表性的多样性和榜样,是导致女性和有色人种在物理学领域的代表性持续严重不足的一个经常被提及的因素。在这项工作中,我们开发了一种基于 Python 的自动化工具,用于识别物理教科书索引和章节中提到的科学家的姓名和人口统计学特征,使物理教科书的作者、出版商和用户能够快速分析这些教科书的人口统计学特征。我们使用标准的机器学习指标对自动化工具进行了定量验证,获得了较高的准确度、精确度、召回率和 F1 分数。然后,我们使用该工具演示了众多潜在应用中的两个:研究在一个具有代表性的四年制本科物理专业课程中使用的全部教科书中提到了哪些人的作品,以及分析在精选的十本物理入门教科书中提到的科学家的人口统计学特征。这两项样本分析都得出了相似的结论,即本著作中研究的本科物理教科书绝大多数都侧重于欧洲、英国和北美裔白人男性的工作。这项研究表明,物理教育界(包括教科书出版商、作者和采用者)迫切需要共同努力,扩大我们对物理学的描述,以反映历史上和当代在这一领域工作的科学家的巨大多样性。
期刊介绍:
The mission of the American Journal of Physics (AJP) is to publish articles on the educational and cultural aspects of physics that are useful, interesting, and accessible to a diverse audience of physics students, educators, and researchers. Our audience generally reads outside their specialties to broaden their understanding of physics and to expand and enhance their pedagogical toolkits at the undergraduate and graduate levels.