Pub Date : 2024-04-10DOI: 10.1177/08944393241246282
David Levine, Tali Gazit
This study examines the role of information sources in the ultra-Orthodox (Haredi) Jewish community’s coping with the coronavirus (COVID-19) pandemic in Israel by comparing their use of digital versus traditional information platforms. The study examined coping with COVID-19, considering explanatory variables such as Community Sense of Coherence (C-SOC), Internet usage, and other demographic variables. Using an online survey, 212 participants responded who identified as ultra-Orthodox and had access to the Internet, of which 47.2% were women and 52.8% were men, with a mean age of 37.66 ( SD = 12.60). Findings showed that the emotional and cognitive coping levels of members of ultra-Orthodox society with COVID-19 utilizing digital information sources were significantly better than those among community members using traditional information sources. Furthermore, the more the Internet was used for information or social needs, the more digital information sources helped community members cope with the crisis from an emotional and cognitive viewpoint. Conversely, the more participants felt that ultra-Orthodox society is a significant factor that helps them face life’s challenges (C-SOC), the better they coped with the pandemic utilizing traditional information sources. This study presents a novel, previously unstudied approach to ultra-Orthodox society’s coping methods with a worldwide crisis, whether through digital or traditional information sources. The study’s findings emphasize the need to make reliable and timely digital information accessible to this community, especially during a crisis, while respecting the culture and values of ultra-Orthodox society.
{"title":"Unorthodox Information Sources of Coping With the COVID-19 Crisis in the Ultra-Orthodox Society","authors":"David Levine, Tali Gazit","doi":"10.1177/08944393241246282","DOIUrl":"https://doi.org/10.1177/08944393241246282","url":null,"abstract":"This study examines the role of information sources in the ultra-Orthodox (Haredi) Jewish community’s coping with the coronavirus (COVID-19) pandemic in Israel by comparing their use of digital versus traditional information platforms. The study examined coping with COVID-19, considering explanatory variables such as Community Sense of Coherence (C-SOC), Internet usage, and other demographic variables. Using an online survey, 212 participants responded who identified as ultra-Orthodox and had access to the Internet, of which 47.2% were women and 52.8% were men, with a mean age of 37.66 ( SD = 12.60). Findings showed that the emotional and cognitive coping levels of members of ultra-Orthodox society with COVID-19 utilizing digital information sources were significantly better than those among community members using traditional information sources. Furthermore, the more the Internet was used for information or social needs, the more digital information sources helped community members cope with the crisis from an emotional and cognitive viewpoint. Conversely, the more participants felt that ultra-Orthodox society is a significant factor that helps them face life’s challenges (C-SOC), the better they coped with the pandemic utilizing traditional information sources. This study presents a novel, previously unstudied approach to ultra-Orthodox society’s coping methods with a worldwide crisis, whether through digital or traditional information sources. The study’s findings emphasize the need to make reliable and timely digital information accessible to this community, especially during a crisis, while respecting the culture and values of ultra-Orthodox society.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140545500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-14DOI: 10.1177/08944393241235175
Jakob Mökander, Ralph Schroeder
In this paper, we first frame the use of artificial intelligence (AI) systems in the public sector as a continuation and intensification of long-standing rationalization and bureaucratization processes. Drawing on Weber, we understand the core of these processes to be the replacement of traditions with instrumental rationality, that is, the most calculable and efficient way of achieving any given policy objective. Second, we demonstrate how much of the criticisms, both among the public and in scholarship, directed towards AI systems spring from well-known tensions at the heart of Weberian rationalization. To illustrate this point, we introduce a thought experiment whereby AI systems are used to optimize tax policy to advance a specific normative end: reducing economic inequality. Our analysis shows that building a machine-like tax system that promotes social and economic equality is possible. However, our analysis also highlights that AI-driven policy optimization (i) comes at the exclusion of other competing political values, (ii) overrides citizens’ sense of their (non-instrumental) obligations to each other, and (iii) undermines the notion of humans as self-determining beings. Third, we observe that contemporary scholarship and advocacy directed towards ensuring that AI systems are legal, ethical, and safe build on and reinforce central assumptions that underpin the process of rationalization, including the modern idea that science can sweep away oppressive systems and replace them with a rule of reason that would rescue humans from moral injustices. That is overly optimistic: science can only provide the means – it cannot dictate the ends. Nonetheless, the use of AI in the public sector can also benefit the institutions and processes of liberal democracies. Most importantly, AI-driven policy optimization demands that normative ends are made explicit and formalized, thereby subjecting them to public scrutiny, deliberation, and debate.
{"title":"Artificial Intelligence, Rationalization, and the Limits of Control in the Public Sector: The Case of Tax Policy Optimization","authors":"Jakob Mökander, Ralph Schroeder","doi":"10.1177/08944393241235175","DOIUrl":"https://doi.org/10.1177/08944393241235175","url":null,"abstract":"In this paper, we first frame the use of artificial intelligence (AI) systems in the public sector as a continuation and intensification of long-standing rationalization and bureaucratization processes. Drawing on Weber, we understand the core of these processes to be the replacement of traditions with instrumental rationality, that is, the most calculable and efficient way of achieving any given policy objective. Second, we demonstrate how much of the criticisms, both among the public and in scholarship, directed towards AI systems spring from well-known tensions at the heart of Weberian rationalization. To illustrate this point, we introduce a thought experiment whereby AI systems are used to optimize tax policy to advance a specific normative end: reducing economic inequality. Our analysis shows that building a machine-like tax system that promotes social and economic equality is possible. However, our analysis also highlights that AI-driven policy optimization (i) comes at the exclusion of other competing political values, (ii) overrides citizens’ sense of their (non-instrumental) obligations to each other, and (iii) undermines the notion of humans as self-determining beings. Third, we observe that contemporary scholarship and advocacy directed towards ensuring that AI systems are legal, ethical, and safe build on and reinforce central assumptions that underpin the process of rationalization, including the modern idea that science can sweep away oppressive systems and replace them with a rule of reason that would rescue humans from moral injustices. That is overly optimistic: science can only provide the means – it cannot dictate the ends. Nonetheless, the use of AI in the public sector can also benefit the institutions and processes of liberal democracies. Most importantly, AI-driven policy optimization demands that normative ends are made explicit and formalized, thereby subjecting them to public scrutiny, deliberation, and debate.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140142191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-23DOI: 10.1177/08944393241235182
Anne Reinhardt, Sophie Mayen, Claudia Wilhelm
Mobile Experience Sampling (MES) is a promising tool for understanding youth digital media use and its effects. Unfortunately, the method suffers from high levels of missing data. Depending on whether the data is randomly or non-randomly missing, it can have severe effects on the validity of findings. For this reason, we investigated predictors of non-response in an MES study on displacement effects of digital media use on adolescents’ well-being and academic performance ( N = 347). Multilevel binary logistic regression identified significant influencing factors of response odds, such as afternoon beeps and being outside. Importantly, adolescents with poorer school grades were more likely to miss beeps. Because this missingness was related to the outcome variable, modern missing data methods such as multiple imputation should be applied before analyzing the data. Understanding the reasons for non-response can be seen as the first step to preventing, minimizing, and handling missing data in MES studies, ultimately ensuring that the collected data is fully utilized to draw accurate conclusions.
移动体验取样（MES）是了解青少年数字媒体使用情况及其影响的一种很有前途的工具。遗憾的是，这种方法存在大量数据缺失的问题。根据数据是随机缺失还是非随机缺失，缺失数据会严重影响研究结果的有效性。因此，我们在一项关于数字媒体的使用对青少年幸福感和学习成绩的影响的多层次调查研究（N = 347）中调查了未回应的预测因素。多层次二元逻辑回归确定了影响响应几率的重要因素，如下午的哔哔声和在户外。重要的是，学习成绩较差的青少年更有可能错过提示音。由于这种缺失与结果变量有关，因此在分析数据前应采用多重估算等现代缺失数据方法。了解无响应的原因可被视为预防、尽量减少和处理 MES 研究中数据缺失的第一步，最终确保收集到的数据得到充分利用，从而得出准确的结论。
{"title":"Uncovering the Missing Pieces: Predictors of Nonresponse in a Mobile Experience Sampling Study on Media Effects Among Youth","authors":"Anne Reinhardt, Sophie Mayen, Claudia Wilhelm","doi":"10.1177/08944393241235182","DOIUrl":"https://doi.org/10.1177/08944393241235182","url":null,"abstract":"Mobile Experience Sampling (MES) is a promising tool for understanding youth digital media use and its effects. Unfortunately, the method suffers from high levels of missing data. Depending on whether the data is randomly or non-randomly missing, it can have severe effects on the validity of findings. For this reason, we investigated predictors of non-response in an MES study on displacement effects of digital media use on adolescents’ well-being and academic performance ( N = 347). Multilevel binary logistic regression identified significant influencing factors of response odds, such as afternoon beeps and being outside. Importantly, adolescents with poorer school grades were more likely to miss beeps. Because this missingness was related to the outcome variable, modern missing data methods such as multiple imputation should be applied before analyzing the data. Understanding the reasons for non-response can be seen as the first step to preventing, minimizing, and handling missing data in MES studies, ultimately ensuring that the collected data is fully utilized to draw accurate conclusions.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139939049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-08DOI: 10.1177/08944393241227868
Bernhard Clemm von Hohenberg, Sebastian Stier, Ana S. Cardenal, Andrew M. Guess, Ericka Menchen-Trevino, Magdalena Wojcieszak
The use of individual-level browsing data, that is, the records of a person’s visits to online content through a desktop or mobile browser, is of increasing importance for social scientists. Browsing data have characteristics that raise many questions for statistical analysis, yet to date, little hands-on guidance on how to handle them exists. Reviewing extant research, and exploring data sets collected by our four research teams spanning seven countries and several years, with over 14,000 participants and 360 million web visits, we derive recommendations along four steps: preprocessing the raw data; filtering out observations; classifying web visits; and modelling browsing behavior. The recommendations we formulate aim to foster best practices in the field, which so far has paid little attention to justifying the many decisions researchers need to take when analyzing web browsing data.
{"title":"Analysis of Web Browsing Data: A Guide","authors":"Bernhard Clemm von Hohenberg, Sebastian Stier, Ana S. Cardenal, Andrew M. Guess, Ericka Menchen-Trevino, Magdalena Wojcieszak","doi":"10.1177/08944393241227868","DOIUrl":"https://doi.org/10.1177/08944393241227868","url":null,"abstract":"The use of individual-level browsing data, that is, the records of a person’s visits to online content through a desktop or mobile browser, is of increasing importance for social scientists. Browsing data have characteristics that raise many questions for statistical analysis, yet to date, little hands-on guidance on how to handle them exists. Reviewing extant research, and exploring data sets collected by our four research teams spanning seven countries and several years, with over 14,000 participants and 360 million web visits, we derive recommendations along four steps: preprocessing the raw data; filtering out observations; classifying web visits; and modelling browsing behavior. The recommendations we formulate aim to foster best practices in the field, which so far has paid little attention to justifying the many decisions researchers need to take when analyzing web browsing data.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139791489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-08DOI: 10.1177/08944393241227868
Bernhard Clemm von Hohenberg, Sebastian Stier, Ana S. Cardenal, Andrew M. Guess, Ericka Menchen-Trevino, Magdalena Wojcieszak
The use of individual-level browsing data, that is, the records of a person’s visits to online content through a desktop or mobile browser, is of increasing importance for social scientists. Browsing data have characteristics that raise many questions for statistical analysis, yet to date, little hands-on guidance on how to handle them exists. Reviewing extant research, and exploring data sets collected by our four research teams spanning seven countries and several years, with over 14,000 participants and 360 million web visits, we derive recommendations along four steps: preprocessing the raw data; filtering out observations; classifying web visits; and modelling browsing behavior. The recommendations we formulate aim to foster best practices in the field, which so far has paid little attention to justifying the many decisions researchers need to take when analyzing web browsing data.
{"title":"Analysis of Web Browsing Data: A Guide","authors":"Bernhard Clemm von Hohenberg, Sebastian Stier, Ana S. Cardenal, Andrew M. Guess, Ericka Menchen-Trevino, Magdalena Wojcieszak","doi":"10.1177/08944393241227868","DOIUrl":"https://doi.org/10.1177/08944393241227868","url":null,"abstract":"The use of individual-level browsing data, that is, the records of a person’s visits to online content through a desktop or mobile browser, is of increasing importance for social scientists. Browsing data have characteristics that raise many questions for statistical analysis, yet to date, little hands-on guidance on how to handle them exists. Reviewing extant research, and exploring data sets collected by our four research teams spanning seven countries and several years, with over 14,000 participants and 360 million web visits, we derive recommendations along four steps: preprocessing the raw data; filtering out observations; classifying web visits; and modelling browsing behavior. The recommendations we formulate aim to foster best practices in the field, which so far has paid little attention to justifying the many decisions researchers need to take when analyzing web browsing data.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139851476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-05DOI: 10.1177/08944393241227554
Terri L. Towner, Caroline L. Muñoz
Political campaigns are embracing the visual social media platform Instagram. One digital feature, the Story, has taken over feed sharing across social media. A Story is a sequence of images or videos uploaded to a profile that disappear after 24 hours. The Story is a novel feature relatively unexamined in political communications and marketing research. Specifically, it is unclear how Gubernatorial candidates employ the Instagram Story feature in campaigning. To address this gap, we content analyze 730 Instagram Stories drawn from 20 Gubernatorial candidate accounts one week before and after Election Day 2018. Results reveal that over half of the candidates employed the Story feature over the two-week period. The Story content primarily included indoor rallies and speeches rather than outdoor canvassing. Campaigns featured more static images than video in Stories and rarely used interactive features, such as animation, location tags, and emojis. Stories were also geared toward mobilization messages rather than voter support, behind-the-scenes looks, and attack ads. Last, some gender and political party differences were evident, as women and Democratic candidates utilized Instagram more.
{"title":"Tell Me an Instagram Story: Ephemeral Communication and the 2018 Gubernatorial Elections","authors":"Terri L. Towner, Caroline L. Muñoz","doi":"10.1177/08944393241227554","DOIUrl":"https://doi.org/10.1177/08944393241227554","url":null,"abstract":"Political campaigns are embracing the visual social media platform Instagram. One digital feature, the Story, has taken over feed sharing across social media. A Story is a sequence of images or videos uploaded to a profile that disappear after 24 hours. The Story is a novel feature relatively unexamined in political communications and marketing research. Specifically, it is unclear how Gubernatorial candidates employ the Instagram Story feature in campaigning. To address this gap, we content analyze 730 Instagram Stories drawn from 20 Gubernatorial candidate accounts one week before and after Election Day 2018. Results reveal that over half of the candidates employed the Story feature over the two-week period. The Story content primarily included indoor rallies and speeches rather than outdoor canvassing. Campaigns featured more static images than video in Stories and rarely used interactive features, such as animation, location tags, and emojis. Stories were also geared toward mobilization messages rather than voter support, behind-the-scenes looks, and attack ads. Last, some gender and political party differences were evident, as women and Democratic candidates utilized Instagram more.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139804692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-05DOI: 10.1177/08944393241227554
Terri L. Towner, Caroline L. Muñoz
Political campaigns are embracing the visual social media platform Instagram. One digital feature, the Story, has taken over feed sharing across social media. A Story is a sequence of images or videos uploaded to a profile that disappear after 24 hours. The Story is a novel feature relatively unexamined in political communications and marketing research. Specifically, it is unclear how Gubernatorial candidates employ the Instagram Story feature in campaigning. To address this gap, we content analyze 730 Instagram Stories drawn from 20 Gubernatorial candidate accounts one week before and after Election Day 2018. Results reveal that over half of the candidates employed the Story feature over the two-week period. The Story content primarily included indoor rallies and speeches rather than outdoor canvassing. Campaigns featured more static images than video in Stories and rarely used interactive features, such as animation, location tags, and emojis. Stories were also geared toward mobilization messages rather than voter support, behind-the-scenes looks, and attack ads. Last, some gender and political party differences were evident, as women and Democratic candidates utilized Instagram more.
{"title":"Tell Me an Instagram Story: Ephemeral Communication and the 2018 Gubernatorial Elections","authors":"Terri L. Towner, Caroline L. Muñoz","doi":"10.1177/08944393241227554","DOIUrl":"https://doi.org/10.1177/08944393241227554","url":null,"abstract":"Political campaigns are embracing the visual social media platform Instagram. One digital feature, the Story, has taken over feed sharing across social media. A Story is a sequence of images or videos uploaded to a profile that disappear after 24 hours. The Story is a novel feature relatively unexamined in political communications and marketing research. Specifically, it is unclear how Gubernatorial candidates employ the Instagram Story feature in campaigning. To address this gap, we content analyze 730 Instagram Stories drawn from 20 Gubernatorial candidate accounts one week before and after Election Day 2018. Results reveal that over half of the candidates employed the Story feature over the two-week period. The Story content primarily included indoor rallies and speeches rather than outdoor canvassing. Campaigns featured more static images than video in Stories and rarely used interactive features, such as animation, location tags, and emojis. Stories were also geared toward mobilization messages rather than voter support, behind-the-scenes looks, and attack ads. Last, some gender and political party differences were evident, as women and Democratic candidates utilized Instagram more.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139864261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-23DOI: 10.1177/08944393231225526
Elisa Maria Campos
Data is the new asset of the current digital revolution. It is heralded as the “new oil” that will transform the world and function as a magic tool for development policies, with great potential to solve global health dilemmas. However, deep societal inequalities give datafication the risk of escalating disparities through data policies instead of solving them. The pandemic unmasked the price to pay for ignoring deep inequalities, helping this research to answer the question: “How did inequalities impact data policies for the Covid-19 pandemic in Brazil?” To investigate this link, the author develops a theoretical model linking the World-historical model of relational inequalities to the capability approach and data colonization theory. This model sustains the analysis of the data collected in 5 months of participant observation in the Covid-19 Favelas Unified Dashboard plus governmental data analysis and semi-structured interviews with data policymakers for Covid-19 in Brazil. As a result, the author demonstrates how inequalities worked as a trap for data policies and argues that data inequalities go beyond the digital divide. Data inequalities skyrocket vulnerability of the poor, increasing contamination rates, and inhibiting development.
{"title":"The Impact of Inequalities on Data Policies: Favelas Unified Dashboard Case Study","authors":"Elisa Maria Campos","doi":"10.1177/08944393231225526","DOIUrl":"https://doi.org/10.1177/08944393231225526","url":null,"abstract":"Data is the new asset of the current digital revolution. It is heralded as the “new oil” that will transform the world and function as a magic tool for development policies, with great potential to solve global health dilemmas. However, deep societal inequalities give datafication the risk of escalating disparities through data policies instead of solving them. The pandemic unmasked the price to pay for ignoring deep inequalities, helping this research to answer the question: “How did inequalities impact data policies for the Covid-19 pandemic in Brazil?” To investigate this link, the author develops a theoretical model linking the World-historical model of relational inequalities to the capability approach and data colonization theory. This model sustains the analysis of the data collected in 5 months of participant observation in the Covid-19 Favelas Unified Dashboard plus governmental data analysis and semi-structured interviews with data policymakers for Covid-19 in Brazil. As a result, the author demonstrates how inequalities worked as a trap for data policies and argues that data inequalities go beyond the digital divide. Data inequalities skyrocket vulnerability of the poor, increasing contamination rates, and inhibiting development.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139605138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-17DOI: 10.1177/08944393241227662
Weijie Huang, Xi Chen
The democratization of technology to re-create content and make that content publicly available has spurred a wave of user-generated content (UGC), which has produced remarkable social and economic benefits. However, under current copyright law, UGC creators face the dilemma of being deterred from creating UGC because of the risk of copyright infringement, copyright owners can rarely obtain remuneration from UGC, and UGC platforms profit from UGC without being held liable for copyright infringement. Recent proposals to extend fair use and compulsory licenses to UGC creators and impose direct liability on UGC platforms cannot solve the UGC dilemma due to the inadequate or unreasonable regulation of UGC platforms. This study aims to solve the UGC dilemma by proposing a non-exploitative UGC levy on UGC platforms. We demonstrate the necessity of the non-exploitative UGC levy by conducting a comparative study of existing proposals and illustrate the feasibility of the non-exploitative UGC levy through an institutional analysis of its framework and enforcement mechanisms. Justification of the proposed levy and responses to possible criticism are also provided. The levy scheme also provides inspiration for how copyright law can address burgeoning artificial intelligence-generated content (AIGC).
再创作内容并将其公开的技术民主化推动了用户生成内容（UGC）的浪潮，产生了显著的社会和经济效益。然而，根据现行的版权法，UGC 创作者面临着两难的境地：创作者因版权侵权风险而不敢创作 UGC，版权所有者很少能从 UGC 中获得报酬，而 UGC 平台从 UGC 中获利却无需承担版权侵权责任。由于对UGC平台的监管不足或不合理，近期提出的将合理使用和强制许可延伸至UGC创作者，以及对UGC平台施加直接法律责任的建议，并不能解决UGC的困境。本研究旨在通过建议对 UGC 平台征收非剥削性 UGC 税来解决 UGC 困境。我们通过对现有建议的比较研究，证明了非剥削性 UGC 征费的必要性，并通过对其框架和执行机制的制度分析，说明了非剥削性 UGC 征费的可行性。此外，还说明了拟议征费的理由，并对可能提出的批评作出回应。该征费计划还为版权法如何应对蓬勃发展的人工智能生成内容（AIGC）提供了启发。
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Pub Date : 2024-01-11DOI: 10.1177/08944393231224540
Alexander Wenz, Florian Keusch, Ruben L. Bach
While digital technology use and skills have typically been measured with surveys, digital behavioral data that are passively collected from individuals’ digital devices have recently emerged as an alternative method of measuring technology usage patterns in a more unobtrusive and detailed way. In this paper, we evaluate how passively collected smartphone usage data compare to self-reported measures of smartphone use, considering the three usage dimensions amount of use, variety of use, and activities of use. Based on a sample of smartphone users in Germany who completed a survey and had a tracking app installed on their smartphone, we find that the alignment between the survey and digital behavioral data varies by dimension of smartphone use. Whereas amount of use is considerably overreported in the survey data, variety of use aligns more closely across the two data sources. For activities of use, the alignment differs by type of activity. The results also show that the alignment between survey and digital behavioral data is systematically related to individuals’ sociodemographic characteristics, including age, gender, and educational attainment. Finally, latent class analyses conducted separately for the survey and digital behavioral data suggest similar typologies of smartphone use, although the overlap between the typologies on the individual level is rather small.
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