The myth of the “data‐driven” society: Exploring the interactions of data interfaces,circulations, and abstractions

The prominence of data and data technologies in society, such as algorithms, social media, mobile technology, and artificial intelligence, have heralded numerous claims of the revolutionary potential of these systems. From public policy, to business management, to scientific research, a “data‐driven” society is apparently imminent—or currently happening—where “objective” and asocial data systems are believed to be comprehensively improving human life. Through a review of existing sociological literature, in this article, we critically examine the relationship between data and society and propose a new model for understanding these dynamics. “Using the concept of the informatic,” we argue the relationship between data and society can be understood as representing the interaction of several different social trends around data; that of data interfaces (that connect individuals to digital contexts), data circulation (trends in the movement and storage of data), and data abstraction (data manipulation practices). Data and data technologies are founded to be entwined and embedded in numerous social relationships, and while not all are fair and equitable relationships, there is ample evidence of the deeply social nature of data across many streams of social life. Our three‐part informatic framework allows these complex relationships to be understood in the social dynamic through which they are witnessed and experienced.     

The home visit in child protection social work: Emotion as resource and risk for professional judgement and practice

This article conceptualises the role of emotion in social work home visits. It draws on findings from a qualitative study of initial child protection home visits in the United Kingdom. The research used narrative interviews and focus groups to examine how emotions arising from visits were registered in social workers' narratives. These visits were often challenging; social workers needed to manage their own emotions and those of the family, while at the same time investigating concerns and assessing need. This article identifies seven key emotional experiences associated with the home visit from the perspective of the social worker: going into the unknown; being intrusive; being disliked; fear of harm to self; fear of causing or allowing harm; pain, disgust, and distress; and “absorbing” emotion. It is argued that emotion plays a central role in home visiting and that professionals' emotional responses have important implications for the way they make sense of, and manage, home visits. Emotion is therefore conceptualized as both a potential resource and risk for social workers' professional judgement and practice.     

Modeling Two-state Disease Processes with Random Effects

Many chronic medical conditions are manifested by alternating sojourns in symptom-free and symptomatic states. In many cases, in addition to their relapsing and remitting nature, these conditions lead to worsening disease patterns over time and may exhibit seasonal trends. We develop a mixed-effect two-state model for such disease processes in which covariate effects are modeled multiplicatively on transition intensities. The transition intensities, in turn, are functions of three time scales: the semi-Markov scale involving the backward recurrence time for the cyclical component, the Markov scale for the time trend component, and a seasonal time scale. Multiplicative bivariate log-normal random effects are introduced to accommodate heterogeneity in disease activity between subjects and to admit a possible negative correlation between the transition intensities. Maximum likelihood estimation is carried out using Gauss-Hermite integration and a standard Newton-Raphson procedure. Tests of homogeneity are presented based on score statistics. An application of the methodology to data from a multi-center clinical trial of chronic bronchitis is provided for illustrative purposes.     

Between book and cinema: late Victorian new media

The origins of new media can be located in the late 19th century, in the visual archive of this period. This antecedent of today's new media appeared first within the pages of the printed book and article, and its origin is related to the increasingly database style of organization of print media at this time. The organizational form of the visual archive that appeared within such printed pages, however, can also be connected to developments in the moral sciences, in particular, the investigation by physiologists of the material foundations of both vision and of thought. As both the eye and the brain became part of the same physical model, so the visual archive came to include both the database itself, and its organizational frame, within a single image.     

Voices from Segregated Schooling: towards an inclusive education system

Romantown LEA, like other authorities in England and Wales, is adopting a policy of 'inclusion' in providing education for young disabled people. The reorganisation has involved the closure of Adamston, an all-age school for pupils with physical disabilities. This article addresses the meaning of 'inclusion' from the viewpoint of disabled people who have experienced segregated education. We draw first on the literature to offer an analysis of the documented views of survivors of special schools. Secondly, we explore the views and experiences of Adamston pupils prior to the closure of the school. In presenting what clearly represents a wide range of experiences we seek to take the debate beyond the sterile analysis of 'pros and cons' of special schools. We argue that voices from experiences of segregation are central in constructing 'inclusion' and essential in any process of change towards an education system which is truly inclusive.     

Variable selection and prediction in biased samples with censored outcomes

With the increasing availability of large prospective disease registries, scientists studying the course of chronic conditions often have access to multiple data sources, with each source generated based on its own entry conditions. The different entry conditions of the various registries may be explicitly based on the response process of interest, in which case the statistical analysis must recognize the unique truncation schemes. Moreover, intermittent assessment of individuals in the registries can lead to interval-censored times of interest. We consider the problem of selecting important prognostic biomarkers from a large set of candidates when the event times of interest are truncated and right- or interval-censored. Methods for penalized regression are adapted to handle truncation via a Turnbull-type complete data likelihood. An expectation–maximization algorithm is described which is empirically shown to perform well. Inverse probability weights are used to adjust for the selection bias when assessing predictive accuracy based on individuals whose event status is known at a time of interest. Application to the motivating study of the development of psoriatic arthritis in patients with psoriasis in both the psoriasis cohort and the psoriatic arthritis cohort illustrates the procedure.     

Challenges related to the analytical process in realist evaluation and latest developments on the use of NVivo from a realist perspective

ABSTRACT

Realist evaluation (RE) is a research design increasingly used in program evaluation, that aims to explore and understand the influence of context and underlying mechanisms on intervention or program outcomes. Several methodological challenges, however, are associated with this approach. This article summarizes RE key principles and examines some documented challenges and solutions when analyzing RE data, including the development of Context-Mechanism-Outcome configurations. An analytic method using NVivo features is also presented. This method makes it possible to respond to certain analytic difficulties associated with RE by facilitating the identification of patterns and ensuring transparency in the analytical process.     

Estimation and Classification of BOLD Responses Over Multiple Trials

In this article, we model functional magnetic resonance imaging (fMRI) data for event-related experiment data using a fourth degree spline to fit voxel specific blood oxygenation level-dependent (BOLD) responses. The data are preprocessed for removing long term temporal components such as drifts using wavelet approximations. The spatial dependence is incorporated in the data by the application of 3D Gaussian spatial filter. The methodology assigns an activation score to each trial based on the voxel specific characteristics of the response curve. The proposed procedure has a capability of being fully automated and it produces activation images based on overall scores assigned to each voxel. The methodology is illustrated on real data from an event-related design experiment of visually guided saccades (VGS).