scholarly journals 10. Alternative Data Practices in China

2021 ◽  
pp. 74-83
Author(s):  
Yolanda Jinxin Ma
Keyword(s):  
2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Kaarina Nikunen ◽  
Jenni Hokka

Welfare states have historically been built on values of egalitarianism and universalism and through high taxation that provides free education, health care, and social security for all. Ideally, this encourages participation of all citizens and formation of inclusive public sphere. In this welfare model, the public service media are also considered some of the main institutions that serve the well-being of an entire society. That is, independent, publicly funded media companies are perceived to enhance equality, citizenship, and social solidarity by providing information and programming that is driven by public rather than commercial interest. This article explores how the public service media and their values of universality, equality, diversity, and quality are affected by datafication and a platformed media environment. It argues that the embeddedness of public service media in a platformed media environment produces complex and contradictory dependencies between public service media and commercial platforms. The embeddedness has resulted in simultaneous processes of adapting to social media logics and datafication within public service media as well as in attempts to create alternative public media value-driven data practices and new public media spaces.


2021 ◽  
Vol 8 (1) ◽  
pp. 205395172098203
Author(s):  
Maria I Espinoza ◽  
Melissa Aronczyk

Under the banner of “data for good,” companies in the technology, finance, and retail sectors supply their proprietary datasets to development agencies, NGOs, and intergovernmental organizations to help solve an array of social problems. We focus on the activities and implications of the Data for Climate Action campaign, a set of public–private collaborations that wield user data to design innovative responses to the global climate crisis. Drawing on in-depth interviews, first-hand observations at “data for good” events, intergovernmental and international organizational reports, and media publicity, we evaluate the logic driving Data for Climate Action initiatives, examining the implications of applying commercial datasets and expertise to environmental problems. Despite the increasing adoption of Data for Climate Action paradigms in government and public sector efforts to address climate change, we argue Data for Climate Action is better seen as a strategy to legitimate extractive, profit-oriented data practices by companies than a means to achieve global goals for environmental sustainability.


2021 ◽  
Vol 8 (1) ◽  
pp. 205395172199603
Author(s):  
Nathaniel Tkacz ◽  
Mário Henrique da Mata Martins ◽  
João Porto de Albuquerque ◽  
Flávio Horita ◽  
Giovanni Dolif Neto

This article adapts the ethnographic medium of the diary to develop a method for studying data and related data practices. The article focuses on the creation of one data diary, developed iteratively over three years in the context of a national centre for monitoring disasters and natural hazards in Brazil (Cemaden). We describe four points of focus involved in the creation of a data diary – spaces, interfaces, types and situations – before reflecting on the value of this method. We suggest data diaries (1) are able to capture the informal dimension of data-intensive organisations; (2) enable empirical analysis of the specific ways that data intervene in the unfolding of situations; and (3) as a document, data diaries can foster interdisciplinary and inter-expert dialogue by bridging different ways of knowing data.


2019 ◽  
Vol 3 (CSCW) ◽  
pp. 1-23 ◽  
Author(s):  
Naveena Karusala ◽  
Jennifer Wilson ◽  
Phebe Vayanos ◽  
Eric Rice

PLoS ONE ◽  
2014 ◽  
Vol 9 (8) ◽  
pp. e104798 ◽  
Author(s):  
Alberto Pepe ◽  
Alyssa Goodman ◽  
August Muench ◽  
Merce Crosas ◽  
Christopher Erdmann

2018 ◽  
Vol 54 (4) ◽  
pp. 559-588
Author(s):  
Rachel Roegman ◽  
Ala Samarapungavan ◽  
Yukiko Maeda ◽  
Gary Johns

Purpose: We explored the practices and understandings around using disaggregated data to inform instruction of 18 principals from three Midwestern school districts. Research Method: This qualitative study used one-on-one semistructured interviews with the principals focusing on how they disaggregate data in practice. The protocol included general questions about principals’ data practices as well as specific questions around disaggregation. Initial inductive coding began with principals’ direct responses to specific questions around disaggregation, and then emerging themes were used to analyze the entire transcripts. Findings: Participants were more likely to talk about disaggregation in relation to performance (by teacher, by grade level, etc.) than by subgroup (by race/ethnicity, by gender, etc.). Further analysis highlighted principals’ purposes for disaggregating data that focused on identifying low performance on standards-based assessments, as well as the challenges they faced, particularly in terms of technical skills and software. Implications for Research and Practice: We conclude with a discussion of how disaggregation could support or challenge equity-focused leadership, with implications for policy, practice, and preparation. We consider the role of the principal in identifying inequitable patterns versus focusing on individual students, and different ways that equity can become part of regular leadership practice.


2021 ◽  
Vol 2021 (2) ◽  
pp. 88-110
Author(s):  
Duc Bui ◽  
Kang G. Shin ◽  
Jong-Min Choi ◽  
Junbum Shin

Abstract Privacy policies are documents required by law and regulations that notify users of the collection, use, and sharing of their personal information on services or applications. While the extraction of personal data objects and their usage thereon is one of the fundamental steps in their automated analysis, it remains challenging due to the complex policy statements written in legal (vague) language. Prior work is limited by small/generated datasets and manually created rules. We formulate the extraction of fine-grained personal data phrases and the corresponding data collection or sharing practices as a sequence-labeling problem that can be solved by an entity-recognition model. We create a large dataset with 4.1k sentences (97k tokens) and 2.6k annotated fine-grained data practices from 30 real-world privacy policies to train and evaluate neural networks. We present a fully automated system, called PI-Extract, which accurately extracts privacy practices by a neural model and outperforms, by a large margin, strong rule-based baselines. We conduct a user study on the effects of data practice annotation which highlights and describes the data practices extracted by PI-Extract to help users better understand privacy-policy documents. Our experimental evaluation results show that the annotation significantly improves the users’ reading comprehension of policy texts, as indicated by a 26.6% increase in the average total reading score.


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