Taken together, the five papers offer diversified perspectives for both understanding and critically assessing emergent forms of datafied living. Finally, Nick Couldry, Andreas Hepp and Jun Yu (LSE, UK, and University of Bremen, Germany) reflect the different imaginations of datafied living: on the one hand, the active imagination of pioneer communities (the Maker and Quantifed Self movements) and on the other hand, the repressed imagination of the facts of data collection in public ‘big data’ discourse. Andrew Iliadis (Temple University, USA) investigates ‘data forging’ to provide a critical assessment of ‘datasmith’ ontologies, ontologists, and ontology-making practices. LSE Graduate Economic History Seminar, The London School of Economics and Political Science, London. Examining the Chinese Sesame Credit – one of the most prominent prototypes of its sort – Alison Hearn (University of Western Ontario, Canada) discusses the potential effects of living with credit scoring. The second paper presented by Göran Bolin (Södertörn University, Sweden) reflects on how the deeper penetration of algorithmically generated metrics into our life-worlds produces a new environment in which we live. In the first paper, Joseph Turow (University of Pennsylvania, USA) analyzes how the multifaceted retailing activities are reshaping the ways companies construct shoppers, and creating a new environment of discrimination through which shoppers will be purchasing products. More specifically, we will discuss five different dimensions of datafied living: shopping, the metricated mindset, credit scoring, data forging and imaginations of datafied living in times of deep mediatization. Referring to such examples, the panel will reflect on datafied living from multiple perspectives that each take a critical point of view, so as to get a sense of this transformation’s complexity. There are already many examples for this in everyday life. 25: 167-178 ( 2023) i32 Jun Bao, Buyu Liu, Jianping Fan, Jun Yu: GLOW: Global Layout Aware Attacks for Object Detection. Therefore, datafied living means that everyday practices are related to data in a constitutive way. j122 Yan Yang, Jun Yu, Jian Zhang, Weidong Han, Hanliang Jiang, Qingming Huang: Joint Embedding of Deep Visual and Semantic Features for Medical Image Report Generation. In times of deep mediatization (Couldry / Hepp 2017), ‘living’ is deeply entangled with digital media and their infrastructures, which continuously produce, assess and communicate data back and forth. Investigating ‘living’ entails not focusing on a single practice of media use but rather researching the range of everyday practices overall. This becomes possible as more and more media and media business models rely on algorithmic processing of data extracted from everyday life Besides ‘tools’ of communication, digital devices and platforms also become generators of data. Datafication means the representation of social life through computerized data (Schäfer & van Es, 2017 van Dijck, 2014). In terms of data analysis tools: intelligent data sampling using compressive sensing, large-scale environmental data model, multimodal image processing, tree growth models, and general modelling of biological populations in space and time.Remove from Personal Schedule Datafied Living: The Everyday of Datafication Sun, May 27, 15:30 to 16:45, Hilton Old Town, Floor: M, Mozart Iĭatafied living is an emerging new ‘way of life’ (Williams, 1971) that is based on datafication. - Best Overall Performance in Year 3 (2019) - Best Performance in Equity and Trusts, Evidence Law, Company Law and Family Law (2019) - Raja, Darryl & Loh Award for the Best Overall Performance in Year 2 (2018) - Sivananthan Award for the Best Performance in Criminal. ![]() Regarding the statistical learning and inference studied: statistical learning with sparsity, compressive sensing, mathematics of data science, hierarchical spatiotemporal modelling, nonparametric density/intensity estimation and smoothing techniques, statistical inference for hidden Markov models and random fields, summary statistics for point processes, and wavelet theory applied to signal and image analys. Share on Facebook Share on Twitter Share on LinkedIn Share on Pinterest Share via Email Print this Page Assistant Professor of Medicine Profile Menu +-Background Education Background Titles. ![]() We work on tackling theoretical data science problems and developing statistical learning methods for solving real-life problems, which originate from various application areas, including atmospheric icing, automobile industry, biomedical engineering, climate research, epidemiology, forestry, geochemistry and hydrology, radiation oncology, spatial ecology, sports science, and transportation. Jun Yu has recently passed his PhD viva, with a thesis entitled Social Solidarity in the Age of Social Media and. I am leading the research group on statistical learning and inference for spatiotemporal data. UCL is consistently ranked as one of the top ten universities in the world (QS World University Rankings 2010-2022) and is No.2 in the UK for research power.
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |