Big Data & Society最新文献

Is there a role of the kidney failure risk equation in optimizing timing of vascular access creation in pre-dialysis patients? 肾衰竭风险方程对透析前患者建立血管通路的最佳时机是否有作用？

1区社会学

Big Data & Society Pub Date : 2023-11-01 Epub Date: 2022-03-27 DOI: 10.1177/11297298221084799

Külli Kuningas, Stephanie Stringer, Paul Cockwell, Aurangzaib Khawaja, Nicholas Inston

{"title":"Is there a role of the kidney failure risk equation in optimizing timing of vascular access creation in pre-dialysis patients?","authors":"Külli Kuningas, Stephanie Stringer, Paul Cockwell, Aurangzaib Khawaja, Nicholas Inston","doi":"10.1177/11297298221084799","DOIUrl":"10.1177/11297298221084799","url":null,"abstract":"Background: The aims of this study were to assess the utility of using the Kidney Failure Risk Equation (KFRE) as an indicator to guide timing of vascular access creation in pre-dialysis patients.Materials and methods: Patients referred for vascular access creation had KFRE calculated at the time of assessment and compared to standard criteria for referral. Receiver operating characteristic curves were produced for each parameter. The outcomes at 3 months, 6 months, and 1 year were used as time points for analysis.Results: Two hundred and three patients were assessed, and full data sets were available on 190 (94.6%). Access was created in 156 patients (82.1%) with a fistula in 153 (98.7%). Only 65.7% initiated dialysis within the follow up period. Those patients with an AV access created (n = 156) 37 (23.7%) did not reach end stage over the entire follow up period. Of the remaining patients (n = 119) that reached end stage 72.2% (n = 86) started on an AVF/AVG and 27.7% (n = 33) on a CVC. Using ROC analysis for referral eGFR, ACR and KFRE predicting dialysis initiation predictors resulted in C statistics for eGFR, ACR, and KFRE2 of 0.68 (0.58-0.79), 0.75 (0.65-0.84), and 0.72 (0.62-0.81) at 3 months; 0.73 (0.65-0.81), 0.70 (0.62-0.78), and 0.75 (0.67-0.81) at 6 months; and 0.65 (0.57-0.72); 0.67 (0.59-0.75), and 0.68 (0.61-0.77) at 12 months.Conclusions: In a group of patients referred for vascular access creation the predictive models are relatively poor when applied to initiation of dialysis. The application of current guidelines to fistula creation appears to result in a high rate of unnecessary fistula formation and non-use. The study requires further evaluation in a test set of patients to confirm these findings and also identify where such risk based approaches may need modification.","PeriodicalId":47834,"journal":{"name":"Big Data & Society","volume":"2 1","pages":"1305-1313"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87567733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Clickbait or conspiracy? How Twitter users address the epistemic uncertainty of a controversial preprint 标题党还是阴谋?Twitter用户如何应对有争议的预印本的认知不确定性

1区社会学

Big Data & Society Pub Date : 2023-07-01 DOI: 10.1177/20539517231180575

Mareike Bauer, Maximilian Heimstädt, Carlos Franzreb, Sonja Schimmler

{"title":"Clickbait or conspiracy? How Twitter users address the epistemic uncertainty of a controversial preprint","authors":"Mareike Bauer, Maximilian Heimstädt, Carlos Franzreb, Sonja Schimmler","doi":"10.1177/20539517231180575","DOIUrl":"https://doi.org/10.1177/20539517231180575","url":null,"abstract":"Many scientists share preprints on social media platforms to gain attention from academic peers, policy-makers, and journalists. In this study we shed light on an unintended but highly consequential effect of sharing preprints: Their contribution to conspiracy theories. Although the scientific community might quickly dismiss a preprint as insubstantial and ‘clickbaity’, its uncertain epistemic status nevertheless allows conspiracy theorists to mobilize the text as scientific support for their own narratives. To better understand the epistemic politics of preprints on social media platforms, we studied the case of a biomedical preprint, which was shared widely and discussed controversially on Twitter in the wake of the coronavirus disease 2019 pandemic. Using a combination of social network analysis and qualitative content analysis, we compared the structures of engagement with the preprint and the discursive practices of scientists and conspiracy theorists. We found that despite substantial engagement, scientists were unable to dampen the conspiracy theorists’ enthusiasm for the preprint. We further found that members from both groups not only tried to reduce the preprint's epistemic uncertainty but sometimes deliberately maintained it. The maintenance of epistemic uncertainty helped conspiracy theorists to reinforce their group's identity as skeptics and allowed scientists to express concerns with the state of their profession. Our study contributes to research on the intricate relations between scientific knowledge and conspiracy theories online, as well as the role of social media platforms for new genres of scholarly communication.","PeriodicalId":47834,"journal":{"name":"Big Data & Society","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136260355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Structured like a language model: Analysing AI as an automated subject 像语言模型一样结构化:将AI作为自动化主题进行分析

1区社会学

Big Data & Society Pub Date : 2023-07-01 DOI: 10.1177/20539517231210273

Liam Magee, Vanicka Arora, Luke Munn

{"title":"Structured like a language model: Analysing AI as an automated subject","authors":"Liam Magee, Vanicka Arora, Luke Munn","doi":"10.1177/20539517231210273","DOIUrl":"https://doi.org/10.1177/20539517231210273","url":null,"abstract":"Drawing from the resources of psychoanalysis and critical media studies, in this article we develop an analysis of large language models (LLMs) as ‘automated subjects’. We argue the intentional fictional projection of subjectivity onto LLMs can yield an alternate frame through which artificial intelligence (AI) behaviour, including its productions of bias and harm, can be analysed. First, we introduce language models, discuss their significance and risks, and outline our case for interpreting model design and outputs with support from psychoanalytic concepts. We trace a brief history of language models, culminating with the releases, in 2022, of systems that realise ‘state-of-the-art’ natural language processing performance. We engage with one such system, OpenAI's InstructGPT, as a case study, detailing the layers of its construction and conducting exploratory and semi-structured interviews with chatbots. These interviews probe the model's moral imperatives to be ‘helpful’, ‘truthful’ and ‘harmless’ by design. The model acts, we argue, as the condensation of often competing social desires, articulated through the internet and harvested into training data, which must then be regulated and repressed. This foundational structure can however be redirected via prompting, so that the model comes to identify with, and transfer , its commitments to the immediate human subject before it. In turn, these automated productions of language can lead to the human subject projecting agency upon the model, effecting occasionally further forms of countertransference. We conclude that critical media methods and psychoanalytic theory together offer a productive frame for grasping the powerful new capacities of AI-driven language systems.","PeriodicalId":47834,"journal":{"name":"Big Data & Society","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135856836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The uncontroversial ‘thingness’ of AI 人工智能无可争议的“物性”

1区社会学

Big Data & Society Pub Date : 2023-07-01 DOI: 10.1177/20539517231206794

Lucy Suchman

引用次数: 0

Digital resignation and the datafied welfare state 数字辞职和数据化的福利国家

1区社会学

Big Data & Society Pub Date : 2023-07-01 DOI: 10.1177/20539517231206806

Christoffer Bagger, Arni Már Einarsson, Victoria Andelsman Alvarez, Maja Klausen, Stine Lomborg

引用次数: 0

Gender and the invisibility of care on Wikipedia 性别和维基百科上的隐蔽性

1区社会学

Big Data & Society Pub Date : 2023-07-01 DOI: 10.1177/20539517231210276

Heather Ford, Tamson Pietsch, Kelly Tall

{"title":"Gender and the invisibility of care on Wikipedia","authors":"Heather Ford, Tamson Pietsch, Kelly Tall","doi":"10.1177/20539517231210276","DOIUrl":"https://doi.org/10.1177/20539517231210276","url":null,"abstract":"Digital platforms produce bias and inequality that have a significant impact on peoples’ sense of self, agency and life chances. Wikipedia has largely evaded the criticism of other algorithmic systems like Google search and training databases like ImageNet, but Wikipedia is a critical source of representation in our current era – not only because it is one of the world's most popular websites, but because its data are being used as training data for the AI systems that are increasingly used for decision-making. We conducted an analysis of Wikipedia biographies in a national context, comparing the temporality and subjects of notability between English Wikipedia and the Australian Honours system in order to understand Wikipedia's unique role in the production of notability over the site's 20-year history. Framing Wikipedia as an active producer (rather than a reflection) of notability, we demonstrate that women are more likely to be awarded a Wikipedia page after the award announcements or not at all if their contribution is for labour relating to the caring professions than if their service is for sports, arts and films, politics or the judiciary. We argue that Wikipedia's inability to recognise gendered care work as noteworthy is mirrored in its own practices.","PeriodicalId":47834,"journal":{"name":"Big Data & Society","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135857029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

‘Blockchain for good’: Exploring the notion of social good inside the blockchain scene “区块链为善”:探索区块链场景中的社会公益概念

1区社会学

Big Data & Society Pub Date : 2023-07-01 DOI: 10.1177/20539517231205479

Silvia Semenzin

引用次数: 0

Nothing new under the sun: Medical professional maintenance in the face of artificial intelligence's disruption 太阳底下没有什么新鲜事:面对人工智能的颠覆，医疗专业维护

1区社会学

Big Data & Society Pub Date : 2023-07-01 DOI: 10.1177/20539517231210269

Netta Avnoon, Amalya L Oliver

{"title":"Nothing new under the sun: Medical professional maintenance in the face of artificial intelligence's disruption","authors":"Netta Avnoon, Amalya L Oliver","doi":"10.1177/20539517231210269","DOIUrl":"https://doi.org/10.1177/20539517231210269","url":null,"abstract":"This paper follows the reaction of the radiology profession to artificial intelligence (AI). We examine the effort of radiology as a powerful medical specialty to maintain its professional jurisdiction while allowing AI's disruption. We study the discursive work of radiologists as evident in their academic publications. Our results suggest that radiologists hold simultaneously multiple perspectives in regard to AI, which allow them to be both conservative and innovative in their relations to it: accept it, subordinate it, reject it and surrender to it, all the same time. These perspectives are: (a) to integrate AI tools and skills into the radiology profession by cooperating and coproducing with AI experts while preserving the core values and structures of the radiology profession; (b) to absorb AI into radiology as (yet another) technology, subordinating it to radiologists’ authority; (c) to fight-off the threat made by AI by undermining the legitimacy and capabilities of AI in radiology and strengthening professional boundaries and (d) to assimilate the radiology profession into the field of AI. These perspectives enable radiologists as a powerful medical specialty to engage in a rhetorical dance with the equally powerful AI specialty and challenge techno-optimistic approaches to innovation.","PeriodicalId":47834,"journal":{"name":"Big Data & Society","volume":"158 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135857304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

From rules to examples: Machine learning's type of authority 从规则到例子:机器学习的权威类型

1区社会学

Big Data & Society Pub Date : 2023-07-01 DOI: 10.1177/20539517231188725

Alexander Campolo, Katia Schwerzmann

引用次数: 0

Diversity and neocolonialism in Big Data research: Avoiding extractivism while struggling with paternalism 大数据研究中的多样性和新殖民主义:在与家长式主义斗争的同时避免榨取主义

1区社会学

Big Data & Society Pub Date : 2023-07-01 DOI: 10.1177/20539517231206802

Paula Helm, Amalia de Götzen, Luca Cernuzzi, Alethia Hume, Shyam Diwakar, Salvador Ruiz Correa, Daniel Gatica-Perez

{"title":"Diversity and neocolonialism in Big Data research: Avoiding extractivism while struggling with paternalism","authors":"Paula Helm, Amalia de Götzen, Luca Cernuzzi, Alethia Hume, Shyam Diwakar, Salvador Ruiz Correa, Daniel Gatica-Perez","doi":"10.1177/20539517231206802","DOIUrl":"https://doi.org/10.1177/20539517231206802","url":null,"abstract":"The extractive logic of Big Data-driven technology and knowledge production has raised serious concerns. While most criticism initially focused on the impacts on Western societies, attention is now increasingly turning to the consequences for communities in the Global South. To date, debates have focused on private-sector activities. In this article, we start from the conviction that publicly funded knowledge and technology production must also be scrutinized for their potential neocolonial entanglements. To this end, we analyze the dynamics of collaboration in an European Union-funded research project that collects data for developing a social platform focused on diversity. The project includes pilot sites in China, Denmark, the United Kingdom, India, Italy, Mexico, Mongolia, and Paraguay. We present the experience at four field sites and reflect on the project’s initial conception, our collaboration, challenges, progress, and results. We then analyze the different experiences in comparison. We conclude that while we have succeeded in finding viable strategies to avoid contributing to the dynamics of unilateral data extraction as one side of the neocolonial circle, it has been infinitely more difficult to break through the much more subtle but no less powerful mechanisms of paternalism that we find to be prevalent in data-driven North–South relations. These mechanisms, however, can be identified as the other side of the neocolonial circle.","PeriodicalId":47834,"journal":{"name":"Big Data & Society","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135856204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0