Blinding Data

致盲数据

• 批准号: 2392385
• 金额: --
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2392385
• 关键词: Blinding; Data

My research will take place in a social and political climate in which a proliferation of digital devices, global online platforms, sensors and closed-circuit cameras has enabled the capture of vast amounts information, earning it the appellation "Big Data" and epigrammatic depiction as the "new oil" (Economist, 2017). In school, data is harvested from systems monitoring attendance, from pupils' interaction with apps and learning environments and from baseline and summative testing and examination. There is a burgeoning interest in behavioural and social and emotional information derived from facial recognition technology, wearable devices and data-loggers. Education technology systems harvest student data for modelling aptitude and behaviours in order to predict achievement and personalise learning. Using Artificial Intelligence (AI) and Machine Learning (ML) techniques, they articulate a "child-by-numbers" in a society in which numbers are hard to challenge yet remain amenable to inscrutable algorithmic intervention. My research will explore how these technologies both represent and constitute a powerful mode of "authorised seeing" and governance (Jasanoff, 2015) by the ways in which they construct, measure and control teachers and pupils, raising pressing questions about learning and knowledge in the educational domain. The work will introduce much-needed interdisciplinary perspectives to understand the social and ethical impact of intensive data processing on new generations of students. Whilst there is a growing body of critical work focusing on datafication in Higher Education, my research will address a gap by examining the impact of an unrelenting data-emphasis in secondary schools in the Edinburgh region. Methodology My ontological position is summarised by the belief that nothing is known distinct from the way in which it is known and given value. This belief is important for tracing the complex sociomaterial networks that constitute big data and its claims for supremacy. My position calls for a methodology that troubles notions of discrete and objective data and I will employ a postqualitative approach because of its commitment to rethink traditional research methods, data and analysis. I will draw inspiration from the work of Donna Haraway, Karen Barad and Elizabeth St Pierre among others. I will collaborate with teachers and pupils in three secondary schools in the Edinburgh area on experimental and participatory projects akin to Ruppert's "para-sites" which "combine research, reflection and reporting and a mix of participants" (2018, p.25) using three bespoke methods: "A life in the day of data", "iVersify" and "Wicked!". Postqualitative methods emerge in responsive and responsible ways from the research experience. Their hallmarks include ethnographic sensibilities and being alert to doubt, difficulty, assumption and surprise. I will attend to breakdowns in understanding to avoid a fixed framing of the research material and remain sensitive to the affective and embodied as constituting valid and important data. A postqualitative methodology aims to surface that which is elided and marginalised and to creatively engender new possibilities. References Economist (2017). The world's most valuable resource is no longer oil, but data. Economist [Website]. Available at: https://www.economist.com/leaders/2017/05/06/the-worlds-most-valuable-resource-is-no-longer-oil-but-data Jasanoff, S. (2015). Future imperfect: Science, technology, and the imaginations of modernity. In S. Jasanoff & S.-H. Kim (Eds.), Dreamscapes of modernity: Sociotechnical imaginaries and the fabrication of power, (pp. 1-35). Chicago, IL: University of Chicago Press. Ruppert, E. (2018). 'Sociotechnical imaginaries of Different Data Futures: An experiment in citizen data'. Dutch and Flemish Sociological Association Conference and the Third Van Doorn Lecture. Rotterdam, Netherlands. June 2018.

我的研究将发生在一个社会和政治环境中，在这个环境中，数字设备、全球在线平台、传感器和闭路摄像机的激增使人们能够捕获大量信息，并将其称为“大数据”，并将其描述为“新石油”（《经济学人》，2017年）。在学校，数据来自监控出勤率的系统，学生与应用程序和学习环境的互动，以及基线和总结性测试和考试。人们对来自面部识别技术、可穿戴设备和数据记录器的行为、社交和情感信息的兴趣正在迅速增长。教育技术系统收集学生数据，用于建模能力和行为，以预测成绩和个性化学习。使用人工智能（AI）和机器学习（ML）技术，他们在一个数字难以挑战但仍然可以接受高深莫测的算法干预的社会中表达了一个“数字的孩子”。我的研究将探讨这些技术如何代表并构成一种强大的“授权观看”和治理模式（Jasanoff，2015），通过它们构建，测量和控制教师和学生的方式，提出有关教育领域学习和知识的紧迫问题。这项工作将引入急需的跨学科视角，以了解密集数据处理对新一代学生的社会和道德影响。虽然有越来越多的关键工作集中在高等教育的专业化，我的研究将通过研究在爱丁堡地区的中学无情的数据强调的影响，以解决差距。方法论我的本体论立场可以概括为这样一种信念，即任何事物都不是与它被认识和被赋予价值的方式截然不同的。这一信念对于追踪构成大数据的复杂社会物质网络及其至高无上的地位至关重要。我的立场要求一种方法，困扰离散和客观数据的概念，我将采用后定性方法，因为它致力于重新思考传统的研究方法，数据和分析。我将从Donna Haraway，Karen Barad和Elizabeth St Pierre等人的工作中汲取灵感。我将与爱丁堡地区三所中学的教师和学生合作，开展类似于Ruppert的“para-sites”的实验性和参与性项目，该项目“结合了联合收割机研究，反思和报告以及参与者的组合”（2018年，第25页），使用三种定制方法：“数据时代的生活”，“iVersify”和“Wicked！".后定性方法从研究经验中以响应和负责任的方式出现。他们的特点包括民族志的敏感性和警惕怀疑，困难，假设和惊喜。我将注意理解中的故障，以避免研究材料的固定框架，并对构成有效和重要数据的情感和体现保持敏感。后质化的方法论旨在使那些被忽略和边缘化的东西浮出水面，并创造性地产生新的可能性。参考文献Economist（2017）.世界上最有价值的资源不再是石油，而是数据。经济学人[网站].网址：https://www.economist.com/leaders/2017/05/06/the-worlds-most-valuable-resource-is-no-longer-oil-but-data Jasanoff，S.（2015年）。不完美的未来：科学、技术和现代性的想象。In S. Jasanoff & S.- H. Kim（Eds.），《现代性的梦境：社会技术的秘密与权力的制造》（Dreamscapes of Modernity：Sociotechnical Quaries and the Fabrication of Power）第1-35段）。芝加哥，IL：芝加哥大学出版社. Ruppert，E.（2018年）。“不同数据未来的社会技术基础：公民数据的实验”。荷兰和佛兰芒社会学协会会议和第三次货车多恩讲座。荷兰，鹿特丹。2018年6月。