{"title":"利用机器学习技术扩大科学参与的受众范围","authors":"Fabienne Crettaz von Roten","doi":"10.3389/fcomm.2024.1382952","DOIUrl":null,"url":null,"abstract":"In Switzerland, the need to engage the public in science is rooted in the political system with its tools of initiatives, therefore the broadening of the audience is critically important. Using the 2021 Science and Technology Eurobarometer, we propose solutions by using machine-learning techniques which identified patterns of engagement and the interaction of sociodemographic characteristics that constitute the prediction (1) of lowest level of science engagement and (2) of non-visitors to the science and technology museum. The techniques allow a more precise targeting than traditional segmenttion analyses.","PeriodicalId":31739,"journal":{"name":"Frontiers in Communication","volume":null,"pages":null},"PeriodicalIF":1.5000,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Broadening the audience for science engagement with machine-learning techniques\",\"authors\":\"Fabienne Crettaz von Roten\",\"doi\":\"10.3389/fcomm.2024.1382952\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In Switzerland, the need to engage the public in science is rooted in the political system with its tools of initiatives, therefore the broadening of the audience is critically important. Using the 2021 Science and Technology Eurobarometer, we propose solutions by using machine-learning techniques which identified patterns of engagement and the interaction of sociodemographic characteristics that constitute the prediction (1) of lowest level of science engagement and (2) of non-visitors to the science and technology museum. The techniques allow a more precise targeting than traditional segmenttion analyses.\",\"PeriodicalId\":31739,\"journal\":{\"name\":\"Frontiers in Communication\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2024-05-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Frontiers in Communication\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3389/fcomm.2024.1382952\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMMUNICATION\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Communication","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3389/fcomm.2024.1382952","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMMUNICATION","Score":null,"Total":0}
Broadening the audience for science engagement with machine-learning techniques
In Switzerland, the need to engage the public in science is rooted in the political system with its tools of initiatives, therefore the broadening of the audience is critically important. Using the 2021 Science and Technology Eurobarometer, we propose solutions by using machine-learning techniques which identified patterns of engagement and the interaction of sociodemographic characteristics that constitute the prediction (1) of lowest level of science engagement and (2) of non-visitors to the science and technology museum. The techniques allow a more precise targeting than traditional segmenttion analyses.