Nowadays, protecting trust in social sciences also means engaging in open community dialogue, which helps to safeguard robustness and improve efficiency of research methods. The combination of open data, open review and open dialogue may sound simple but implementation in the real world will not be straightforward. However, in view of Begley and Ellis’s (2012) statement that, “the scientific process demands the highest standards of quality, ethics and rigour,” they are worth implementing. More importantly, they are feasible (...) to work on and likely will help to restore plausibility to social sciences research. Therefore, I feel it likely that the triplet of open data, open review and open dialogue will gradually emerge to become policy requirements regardless of the research funding source. (shrink)

The Handbook of Computational Social Science is a comprehensive reference source for scholars across multiple disciplines. It outlines key debates in the field, showcasing novel statistical modeling and machine learning methods, and draws from specific case studies to demonstrate the opportunities and challenges in CSS approaches. The Handbook is divided into two volumes written by outstanding, internationally renowned scholars in the field. This first volume focuses on the scope of computational social science, ethics, and case studies. It covers (...) a range of key issues, including open science, formal modeling, and the social and behavioral sciences. This volume explores major debates, introduces digital trace data, reviews the changing survey landscape, and presents novel examples of computational social science research on sensing social interaction, social robots, bots, sentiment, manipulation, and extremism in social media. The volume not only makes major contributions to the consolidation of this growing research field, but also encourages growth into new directions. With its broad coverage of perspectives (theoretical, methodological, computational), international scope, and interdisciplinary approach, this important resource is integral reading for advanced undergraduates, postgraduates and researchers engaging with computational methods across the social sciences, as well as those within the scientific and engineering sectors. (shrink)

This article investigates the work of processors who curate and “clean” the data sets that researchers submit to data archives for archiving and further dissemination. Based on ethnographic fieldwork conducted at the data processing unit of a major US social science data archive, I investigate how these data processors work, under which status, and how they contribute to data sharing. This article presents two main results. First, it contributes to the study of invisible (...) technicians in science by showing that the same procedures can make technical work invisible outside and visible inside the archive, to allow peer review and quality control. Second, this article contributes to the social study of scientific data sharing, by showing that the organization of data processing directly stems from the conception that the archive promotes of a valid data set—that is, a data set that must look “pristine” at the end of its processing. After critically interrogating this notion of pristineness, I show how it perpetuates a misleading conception of data as “raw” instead of acknowledging the important contribution of data processors to data sharing and social science. (shrink)

Academia is a competitive environment. Early Career Researchers (ECRs) are limited in experience and resources and especially need achievements to secure and expand their careers. To help with these issues, this book offers a new approach for conducting research using the combination of mindsponge innovative thinking and Bayesian analytics. This is not just another analytics book. 1. A new perspective on psychological processes: Mindsponge is a novel approach for examining the human mind’s information processing mechanism. This conceptual framework is used (...) to construct models in studies. The framework is highly flexible and widely applicable for many different types of information processes. The mindsponge approach can help researchers discover interesting ideas or even formulate their very own theories when investigating psychosocial phenomena. This approach brings a fresh wind to the current landscape of social sciences and humanities (SSH). 2. Easy-to-follow analysis protocol: The Bayesian Mindsponge Framework (BMF analytics) is useful in terms of computing and visualizing power but also easy to learn and apply. Contrary to being intimidating, the Bayesian analytics section of this book is presented in a reader-friendly manner with a detailed yet clear step-by-step procedure. Examples are from published BMF articles, allowing readers to immediately practice the method and quickly create their own applications. With educational purposes in mind, the book is very suitable for ECRs who are looking to innovate their research methods. 3. Advocating for low-cost, high-quality research: Doing science can be very costly. Mindsponge innovative thinking and BMF analytics help produce impactful studies using openly available data on online repositories. This is based on the authors’ previous works and experiences. The book presents examples of employing the open R package bayesvl on secondary data from different sources. With less financial constraints, researchers can have more freedom of thought to pursue their curiosity and creativity. ECRs in low- and middle-income countries may find this aspect crucial in their careers. 4. Support and collaboration: The authors share their insights from experiences in the academic publishing system to help readers get through the processes of manuscript writing and peer-reviewing more easily. The authors are also ready to support other researchers with further inquiries and collaboration opportunities at the following website, mindsponge(dot)info. This book is for: a) ECRs whose only abundant resources are their innovation capacity and strength of will; b) Researchers in SSH who want to explore a novel approach to thinking and study conducting; c) Low- and middle-income countries’ researchers looking for a cost-effective research protocol; and, d) Innovative thinkers who want to turn their interesting thoughts into good publications. (shrink)

Research Ethics, Volume 18, Issue 1, Page 24-38, January 2022. Are social science, cross-border research projects, where recruitment and data collection are carried out remotely, required to follow similar ethical and data-sharing procedures as ‘on-the-ground’ studies that use traditional means of recruitment and participant engagement? This article reflects on our experience of dealing with this question when we had to switch to online data collection due to the restrictions posed by the COVID-19 pandemic, such as the (...) inability to travel or work in person with local communities and collaborators. Using social media platforms and online data collection when conducting research brings many advantages, such as being able to communicate remotely but directly with gatekeepers and collaborators, and in reaching potential participants on a global scale. However, neither the guidelines and advice for conducting ethically sound internet-based research, nor the academic literature focussed on building equitable research partnerships between the Global North and the Global South, offer much information regarding the ethical concerns, or address the grey areas, posed by this type of digital and distanced transnational research. In our experience, conducting research remotely made negotiations of access very challenging due to the politics of positionality between Global North and South researchers, lack of clarity on ethical processes and perceptions of gatekeepers who we could not meet in person. We hope the reflections on, and discussion of, our experience encourage deliberation on the present ethical challenges posed by online and social-media-disseminated data collection, particularly in cross-border circumstances. (shrink)

Big data represents a significant challenge for the social sciences. From a philosophy-of-science perspective, it is important to reflect on related theories and processes for developing them. In this paper, we start by examining different views on the role of theories in big data-related social research. Then, we try to show how big data is related to standards for evaluating theories. We also outline how big data affects theory- and data-based research approaches (...) and the process of theory building. Discussions include a summary of lines of reasoning, limitations, and a proposal for future developmental steps. (shrink)

The ethical and social implications of data mining, algorithmic curation and automation in the context of social media have been of heightened concern for a range of researchers with interests in digital media in recent years, with particular concerns about privacy arising in the context of mobile and locative media. Despite their wide adoption and economic importance, mobile dating apps have received little scholarly attention from this perspective – but they are intense sites of data generation, (...) algorithmic processing, and cross-platform data-sharing; bound up with competing cultures of production, exploitation and use. In this paper, we describe the ways various forms of data are incorporated into, and emerge from, hook-up apps’ business logics, socio-technical arrangements, and cultures of use to produce multiple and intersecting data cultures. We propose a multi-layered research agenda for critical and empirical inquiry into this field, and suggest appropriate conceptual and methodological frameworks for exploring the social and political challenges of data cultures. (shrink)

In this digital age the use of video in social science research has become commonplace. As sophistication has increased along with usability, as spiralling staff costs push out direct observation, the researchers training today are grasping video as a means of coming to terms with the continued pressure to produce accessible research. However, the ‘fit’ of technology with research is far from simple. Ideally placed to offer guidance to developing researchers, this new text draws together the theoretical, methodological and (...) practical issues of effectively using video across the social sciences. This book concentrates on how researchers can benefit from the use of video in their own research, whether it is: Video as representation Video as an aid to reflection Video that generates participation Video, voice and articulation, or Video that acts as a provocation. In turn each of these five central functions is discussed in relation to different stages of the research process, consisting of: Research design Fieldwork and data collection Analysis of data and findings Dissemination. As a practical research tool this book shows how, why and when video should be used, representing an invaluable guide for postgraduate and doctoral students conducting research in the social sciences, as well as any researchers, academics or professionals interested in developing technologically informed research. (shrink)

The humanities and social sciences (HSS) are being turned upside down by advances in artificial intelligence (AI), and their very existence could be threatened. These sciences are being profoundly destabilised by a dual process of naturalisation of social phenomena and fetishisation of numbers, accentuated by the development of AI (part 1). Both STM (science, technology, medicine) and HSS are facing major epistemological challenges, but for the latter they carry the risk of marginalisation (part 2). The humanities (...) and social sciences remain the best equipped to question the social construct represented by the development of AI. However, this essential approach is not enough. We need to ask ourselves: how can the HSS reintroduce interpretation when they have less and less control over how data is put together? Only a balanced partnership between STM and HSS is likely to meet all these challenges (part 3). Using the case of education, which has long been at the forefront of developments in other sectors of social life, we would like to show how and on what priority issues such a partnership can be built (part 4). (shrink)

This paper concerns design thinking (Lawson, 1980), system thinking (systems theory) (von Bertalanffy, 1968), and system dynamics modeling as methodological platforms for analyzing large amounts of qualitative data and transforming it into quantitative mode. The aims of this article are to present an integral (mixed) research process including the design thinking process—a solution oriented approach applicable in the social sciences and humanities which enables to reveal causality in research on societal and behavioral issues. This integral approach is (...) illustrated by an empirical pilot study from art/design-educational environment. (shrink)

In recent years, many qualitative sociologists, anthropologists, and social theorists have critiqued the use of algorithms and other automated processes involved in data science on both epistemological and political grounds. Yet, it has proven difficult to bring these important insights into the practice of data science itself. We suggest that part of this problem has to do with under-examined or unacknowledged assumptions about the relationship between the two fields—ideas about how data science and its critics can (...) and should relate. Inspired by recent work in Science and Technology Studies on interventions, we attempted to stage an encounter in which practicing data scientists were asked to analyze a corpus of critical social science literature about their work, using tools of textual analysis such as co-word and topic modelling. The idea was to provoke discussion both about the content of these texts and the possible limits of such analyses. In this commentary, we reflect on the planning stages of the experiment and how responses to the exercise, from both data scientists and qualitative social scientists, revealed some of the tensions and interactions between the normative positions of the different fields. We argue for further studies which can help us understand what these interdisciplinary tensions turn on—which do not paper over them but also do not take them as given. (shrink)

Assuming that the purpose of research ethics review is to support the ethical conduct and dissemination of good quality research, a question can be raised concerning whether ethics review of research really improves the practice of researchers. Specifically, we might distinguish the activities that go on as part of the review process from those activities that constitute the data collection phase of the research, and ask under what conditions the former have a positive impact on the latter. Two different (...) models of ethics review can be distinguished, term them ‘compliance’ and ‘reflexivity’-based reviews. Compliance-based models of review require researchers to show how they are complying with a given set of rules or protocols for research. Reflexivity-based models require researchers to articulate the ethical issues involved in their research and to explain and justify the way in which they plan to manage them. It is suggested here that there is reason to think that reflexivity-based models of ethics review are more likely to have a positive effect on the practice of social science research than compliance-based reviews. (shrink)

This article proposes that Discourse Analysis (DA) be methodologically characterized as an analytical technique for the social sciences. To do this, it must first be situated in relation to two other methodological tools used for the study of discourse: hermeneutics and Content Analysis. Subsequently, the article will define DA, focusing on one aspect in particular: its compatibility with both qualitative and quantitative research strategies. It will then examine the usefulness of this technique in the process of data (...) construction and finally propose an assessment of this issue. En este artículo nos proponemos caracterizar metodológicamente al Análisis del Discurso (AD) en tanto técnica de análisis de las ciencias sociales. Para ello, primero, lo ubicaremos en relación con otras dos herramientas metodológicas utilizadas para estudiar el discurso: la hermenéutica y el análisis de contenido. Posteriormente, haremos una caracterización del AD, centrándonos en un aspecto en particular: su compatibilidad tanto con estrategias cualitativas como con estrategias cuantitativas de investigación. Estudiaremos la utilidad de esta técnica en el proceso de construcción de los datos. Finalmente, propondremos un balance de la cuestión. (shrink)

Ethical and quality concerns are important concerns in doctoral research programs and often draw the attention of governments, universities governing bodies, academics, and research students. Addressing these concerns is key to maintaining and improving the quality of doctoral research. This study used generic qualitative research design. The sample of the study consisting of seventeen doctoral students and ten doctoral supervisors was selected from social science doctoral programs using purposive sampling technique. Interviews were used as data collection tools. The (...) study aimed at contributing to the areas of quality and ethical processes and outcomes in social science research in the Pakistani context where ethical and quality dimensions in educational research have been relatively less explored. The study indicated several ethical and quality issues in the doctoral research programs including inadequate research supervision and evaluation processes, inadequate course work, low teaching quality, and a focus on getting degrees rather than on obtaining genuine scholarship. Policy makers and higher education institutions may need to deal with issues that lead to a compromise on doctoral research studies in ethical and quality terms. The findings of this study may prove helpful to remedy some of the existing causes of ethical and quality issues in research at the PhD level and may help to produce value added research for social and economic progress. Further research may be conducted to explore possible ethical and quality concerns in natural sciences doctoral programs and to explore possible intervention-mechanisms for ensuring ethical and quality concerns in such programs. (shrink)

Academic and public debates are increasingly concerned with the question whether and how algorithmic decision-making may reinforce social inequality. Most previous research on this topic originates from computer science. The social sciences, however, have huge potentials to contribute to research on social consequences of ADM. Based on a process model of ADM systems, we demonstrate how social sciences may advance the literature on the impacts of ADM on social inequality by uncovering and mitigating (...) biases in training data, by understanding data processing and analysis, as well as by studying social contexts of algorithms in practice. Furthermore, we show that fairness notions need to be evaluated with respect to specific outcomes of ADM systems and with respect to concrete social contexts. Social sciences may evaluate how individuals handle algorithmic decisions in practice and how single decisions aggregate to macro social outcomes. In this overview, we highlight how social sciences can apply their knowledge on social stratification and on substantive domains of ADM applications to advance the understanding of social impacts of ADM. (shrink)

The era of ‘big data’ studies and computational social science has recently given rise to a number of realignments within and beyond the social sciences, where otherwise distinct data formats – digital, numerical, ethnographic, visual, etc. – rub off and emerge from one another in new ways. This article chronicles the collaboration between a team of anthropologists and sociologists, who worked together for one week in an experimental attempt to combine ‘big’ transactional and ‘small’ ethnographic (...) data formats. Our collaboration is part of a larger cross-disciplinary project carried out at the Danish Technical University, where high-resolution transactional data from smartphones allows for recordings of social networks amongst a freshman class. With a parallel deployment of ethnographic fieldwork among the DTU students, this research set-up raises a number of questions concerning how to assemble disparate ‘data-worlds’ and to what epistemological and political effects? To address these questions, a specific social event – a lively student party – was singled out from the broader DTU dataset. Our experimental collaboration used recordings of Bluetooth signals between students’ phones to visualize the ebb and flow of social intensities at the DTU party, juxtaposing these with ethnographic field-notes on shifting party atmospheres. Tracing and reflecting on the process of combining heterogeneous data, the article offers a concrete case of how a ‘stitching together’ of digital and ethnographic data-worlds might take place. (shrink)

The experimental method is designed to secure the reliable attribution of causal relationships by means of controlled comparison across conditions. Doing so, however, depends upon the reduction of uncertainties and inconsistencies in the process of comparison; and this poses particularly significant challenges for the behavioral and social sciences because they work with human subjects, whose malleability and complexity often interact in unexpected ways with experimental manipulations, thus resulting in unpredictable behavior. Drawing on the Science and Technology Studies perspective (...) and one of our authors’ experiences in experimental work, this paper examines how experimental social scientists manage to establish objectivity and standardization in the face of vagaries arising from working with human subjects. In identifying experimental researchers’ solutions to this challenge, we draw on methodological discussions among applied social scientists as naturally occurring data, through which we show how some seemingly mundane practices play essential roles in extracting patterns out of otherwise unpredictable behaviors in the lab. Closely examining such strategies, we reveal the inherent instabilities in the experimental method when adopted in the social sciences and discuss their methodological implications. In conclusion, we make tentative suggestions for escaping the kinds of methodological impasses which we have identified. (shrink)

The large-scale and intensive collection and analysis of digital data (commonly called “Big Data”) has become a common, popular, and consensual research method for the social sciences, as the automation of data collection, mathematization of analysis, and digital objectification reinforce both its efficiency and truth-value. This article opens with a critical review of the literature on data collection and analysis, and summarizes current ethical discussions focusing on these technologies. A semiotic model of data (...) production and circulation is then introduced to problematize the view that digital data has ceased to stand for a formalization method (a possible kind of representation among others), and effectively “becomes the world itself” (a direct presentation of the world outperforming all other modes of representation). Following Charles Sanders Peirce’s semiotics and pragmaticist philosophy, we characterize digitalization as a hypersymbolic semiotic process, and we highlight the naturalization of meaning, the illusion of iconicity, and rhetorical efficiency on which data’s truth value relies within the context of its large-scale, profit-driven, and results-oriented research uses. This outlines some epistemological and ethical implications of data’s visualization, use, and authority, and indicates avenues for critical semiotics of contemporary data science and analysis. (shrink)

Unprecedented advances in the ability to store, analyze, and retrieve data is the hallmark of the information age. Along with enhanced capability to identify meaningful patterns in large data sets, contemporary data science renders many classical models of privacy protection ineffective. Addressing these issues through privacy-sensitive design is insufficient because advanced data science is mutually exclusive with preserving privacy. The special privacy problem posed by data analysis has so far escaped even leading accounts of informational (...) privacy. Here, I argue that accounts of privacy must include norms about information processing in addition to norms about information flow. Ultimately, users need the resources to control how and when personal information is processed and the knowledge to make information decisions about that control. While privacy is an insufficient design constraint, value-sensitive design around control and transparency can support privacy in the information age. (shrink)

Powerful new social science data resources are emerging. One particularly important source is administrative data, which were originally collected for organisational purposes but often contain information that is suitable for social science research. In this paper we outline the concept of reproducible research in relation to micro-level administrative social science data. Our central claim is that a planned and organised workflow is essential for high quality research using micro-level administrative social science data. (...) We argue that it is essential for researchers to share research code, because code sharing enables the elements of reproducible research. First, it enables results to be duplicated and therefore allows the accuracy and validity of analyses to be evaluated. Second, it facilitates further tests of the robustness of the original piece of research. Drawing on insights from computer science and other disciplines that have been engaged in e-Research we discuss and advocate the use of Git repositories to provide a useable and effective solution to research code sharing and rendering social science research using micro-level administrative data reproducible. (shrink)

Agent-Based Models are useful to describe and understand social, economic and spatial systems' dynamics. But, beside the facilities which this methodology offers, evaluation and comparison of simulation models are sometimes problematic. A rigorous conceptual frame needs to be developed. This is in order to ensure the coherence in the chain linking at the one extreme the scientist's hypotheses about the modeled phenomenon and at the other the structure of rules in the computer program. This also systematizes the model design (...) from the thematician conceptual framework as well. The aim is to reflect upon the role that a well defined ontology, based on the crossing of the philosophical and the computer science insights, can play to solve such questions and help the model building. We analyze different conceptions of ontology, introduce the 'ontological test' and show its usefulness to compare models. Then we focus on the model building and show the place of a systematic ABM ontology. The latter process is situated within a larger framework called the 'knowledge framework' in which not only the ontologies but also the notions of theory, model and empirical data take place. At last the relation between emergence and ontology is discussed. (shrink)

Pierre Bourdieu has developed a philosophy of social science, grounded in the phenomenological tradition, which treats knowledge as a practical ability embodied in skilful behaviour, rather than an intellectual capacity for the representation and manipulation of propositional knowledge. He invokes Wittgenstein’s remarks on rule-following as one way of explicating the idea that knowledge is a skill. Bourdieu’s conception of tacit knowledge is a dispositional one, adopted to avoid a perceived dilemma for methodological individualism. That dilemma requires either the explanation (...) of regularities in social behaviour as the result of the tacit representation of procedural rules (‘legalism’) or the self-conscious representation of behavioural goals (‘voluntarism’) by individuals. After explaining the apparent dilemma, I then argue that Wittgenstein’s remarks on rule following actually undermine, rather than support, a dispositional solution. Nonetheless, the philosophy of social science can survive without a dispositional account of knowledge. Such a social science needs, firstly, to embrace one horn of the dilemma, voluntarism, provided that the relevant regularities can be explained as unintended consequences of agents’ self-represented intentions. Secondly, such a social science should treat theorists’ interpretations as unifying generalizations, not hypotheses about the acquisition of tacit knowledge. Finally, where appeal to cognitive psychology can distinguish otherwise equivalent theories in social science, social science should incorporate the data of cognitive psychology concerning tacit mental processes. (shrink)

The data science era is characterized by data-driven automated decision systems (ADS) enabling, through data analytics and machine learning, automated decisions in many contexts, deeply impacting our lives. As such, their downsides and potential risks are becoming more and more evident: technical solutions, alone, are not sufficient and an interdisciplinary approach is needed. Consequently, ADS should evolve into data-informed ADS, which take humans in the loop in all the data processing steps. Data-informed ADS should (...) deal with data responsibly, guaranteeing nondiscrimination with respect to protected groups of individuals. Nondiscrimination can be characterized in terms of different types of properties, like fairness and diversity. While fairness, i.e., absence of bias against minorities, has been widely investigated in machine learning, only more recently this issue has been tackled by considering all the steps of data processing pipelines at the basis of ADS, from data acquisition to analysis. Additionally, fairness is just one point of view of nondiscrimination to be considered for guaranteeing equity: other issues, like diversity, are raising interest from the scientific community due to their relevance in society. This paper aims at critically surveying how nondiscrimination has been investigated in the context of complex data science pipelines at the basis of data-informed ADS, by focusing on the specific data processing tasks for which nondiscrimination solutions have been proposed. (shrink)

Computer models and simulations have become, since the 1960s, an essential instrument for scientific inquiry and political decision making in several fields, from climate to life and social sciences. Philosophical reflection has mainly focused on the ontological status of the computational modeling, on its epistemological validity and on the research practices it entails. But in computational sciences, the work on models and simulations are only two steps of a longer and richer process where operations on data (...) are as important as, and even more time and energy-consuming than modeling itself. Drawing on two study cases—computational embryology and computational epidemiology—this article contributes to filling the gap by focusing on the operations of producing and re-using data in computational sciences. The different phases of the scientific and artisanal work of modelers include data collection, aggregation, homogenization, assemblage, analysis and visualization. The article deconstructs the ideas that data are self-evident informational aggregates and that data-driven approaches are exempted from theoretical work. More importantly, the paper stresses the fact that data are constructed and theory laden not only in their fabrication, but also in their reusing. (shrink)

Accessing research participants within some social institutions for research purposes may involve a simple single administrative event. However, accessing some institutions to conduct research on their data, personnel, clients or service users can be quite complex. Research ethics committee chairpersons frequently field questions from researchers wanting to know when and why gatekeeper permission should be sought. This article examines the role and influence of gatekeepers in formal and organisational settings and explores pragmatic methods to improve understanding and facilitation (...) of this process. Conscientious and well-informed negotiations with gatekeepers are required in order to honour the ethical obligations to conduct appropriate stakeholder engagement before and during research, along with respect for the autonomy of institutions and their employees/clients/service recipients. Provision must be made to identify explicit and implicit gatekeepers to initiate and build collaborative networks that could best support the research process. Careful mutually respectful access agreements which consider the needs and vulnerabilities of both the gatekeeper and the researcher can improve the quality of the scientific data collected. Strategic planning in the research process must take these sometimes complex processes of gatekeeper permission into careful account. (shrink)

The body-oriented approach in the philosophy of cognitive sciences is gaining in importance in the conditions of the formation of new high-tech contexts. The problem of interoception and integration of bodily data into socio-economic processes requires a comprehensive analysis and ethical assessment. This article examines the conceptual foundations of the body-oriented approach and its impact on the essence of cognitive processes. The main advantages and disadvantages of this approach are presented. We consider the methodological conflict zones of the (...) bodily paradigm and traditional methodological attitudes of the cognitive sciences. The system ofinteroceptive sensations is a special «layer» of bodily experience. The social functions of interoceptive sensations are an interdisciplinary research area that focuses on the mechanisms of interaction between the private content of bodily experience and systems of social interaction. The regularity between interoceptive sensations and human behavioral patterns reflects the importance of examining bodily data. Today, the market for bodily data is just beginning to form, but at the same time it is already playing an important role in global digital transformation processes. A multitude of applications and wearable digital devices that track bodily activity and generate gigantic amounts of data are used today by millions of people around the world. The identification of bodily activity leads to the emergence of new forms of interaction between a person and socio-institutional systems, the specific properties of which are also considered in this article. (shrink)

The COVID-19 pandemic and its related policies (e.g., stay at home and social distancing orders) have increased people’s use of digital technology, such as social media. Researchers have, in turn, utilized artificial intelligence to analyze social media data for public health surveillance. For example, through machine learning and natural language processing, they have monitored social media data to examine public knowledge and behavior. This paper explores the ethical considerations of using artificial intelligence to monitor (...) social media to understand the public’s perspectives and behaviors surrounding COVID-19, including potential risks and benefits of an AI-driven approach. Importantly, investigators and ethics committees have a role in ensuring that researchers adhere to ethical principles of respect for persons, beneficence, and justice in a way that moves science forward while ensuring public safety and confidence in the process. (shrink)

Un requisito para demostrar la vigencia de un programa de posgrado es la evaluación de su pertinencia social. El presente trabajo tiene como objetivo describir los resultados de la evaluación de la pertinencia social del programa de maestría Humanidades Médicas. Se muestran sus características esenciales derivadas de los procesos realizados en el Centro para el Desarrollo de las Ciencias Sociales y Humanísticas en Salud; se define la pertinencia social y sus indicadores como conceptos esenciales para la evaluación (...) de los procesos del posgrado; por último, se presenta la metodología y su aplicación en el proceso de evaluación. Se realizó triangulación metodológica de fuentes de datos provenientes de encuestas y entrevistas a profesores, maestrantes de la edición actual y de egresados; además, datos de los informes de autoevaluación y de la comisión técnica en el proceso de evaluación externa para la acreditación acaecida en marzo de 2016. A requisite for demonstrating the validity of a postgraduate program is the assessment of its social relevance. This work aims to describe evaluative results related to the social relevance of the Masters of Science in Medical Humanities degree. The evaluation process was carried out in the Health Humanities and Social Sciences Development Centre of Camaguey. Social relevance and related indicators were defined and considered for this specific master's degree level evaluation. The methodology and its application in the evaluation process are presented. Data triangulation between surveys collected and interviews granted from faculty, current students of the Medical Humanities program, and graduates of the department was utilized. Additionally, data from self-evaluation reports and from the external evaluation technical commission for accreditation in March 2016 were considered in the data triangulation. (shrink)

This groundbreaking, open access volume analyses and compares data practices across several fields through the analysis of specific cases of data journeys. It brings together leading scholars in the philosophy, history and social studies of science to achieve two goals: tracking the travel of data across different spaces, times and domains of research practice; and documenting how such journeys affect the use of data as evidence and the knowledge being produced. The volume captures the opportunities, (...) challenges and concerns involved in making data move from the sites in which they are originally produced to sites where they can be integrated with other data, analysed and re-used for a variety of purposes. The in-depth study of data journeys provides the necessary ground to examine disciplinary, geographical and historical differences and similarities in data management, processing and interpretation, thus identifying the key conditions of possibility for the widespread data sharing associated with Big and Open Data. The chapters are ordered in sections that broadly correspond to different stages of the journeys of data, from their generation to the legitimisation of their use for specific purposes. Additionally, the preface to the volume provides a variety of alternative “roadmaps” aimed to serve the different interests and entry points of readers; and the introduction provides a substantive overview of what data journeys can teach about the methods and epistemology of research. (shrink)

This book tries to explain how decisions to act develop in the mind. Emphasis is on group decisions not only of the present but also from the past, where laboratory techniques can t apply. What emerges is a description of a process rather than the definition of a word. The description points to kinds of data that need special consideration: data regarding ideas of right and wrong, cultural traditions, emotional packaging.".

Scientific knowledge is no longer transmitted solely through a one-way channel of communication from scientific communities to `lay' readers through the knowledge transmission `chain'. Communication between the two communities has now been extended into media and everyday social discourse where it crops up in news debates about issues such as public health and food safety. In this process, scientific academic discourse has lost much of its original form. This article examines part of the current research at the Centre de (...) recherche sur les discours ordinaires et spécialisés. It provides evidence of new discursive forms adopted by science in media and social discourse, such as the emergence of enunciative standpoints, the redefinition of the role of the journalist as mediator between science and the general public, the use of specific forms of intertextuality and the interweaving of scientific elements in general social discussions. This research on new data implies renewed definitions of certain concepts used in linguistic descriptions: discursive meaning and signification, interdiscursive memory bank, etc. These recent forms of discourse have brought about a change in the status of science so that science has now become an object of debate in the public arena — a sign of its crucial social role. (shrink)

Research on privacy practices in digital environments has oftentimes discovered a paradoxical relationship between users’ discursive appraisal of privacy and their actual practices: the “privacy paradox.” The emergence of this paradox prompts us to conduct ethnography of a health and fitness platform in order to flesh out the structural mechanisms generating this paradox. We provide an ethnographic analysis of surveillance capitalism in action that relates front-end practices empirically to the data economy’s back-end operations to show how this material-semiotic setup (...) elicits users’ desire to become self-determined subjects in a way that makes them amenable as objects of behavioral engineering. We combine different ethnographic methods and materials to specify how different types of values are produced and translated on the investigated platform. The latter offers users the values of self-mastery and social visibility. However, the data generated in this process serve to translate these values into the value of economic amenability and thereupon ultimately into economic profit. What gets lost in translation, though, is the front-end promise of self-mastery. It is these structural mechanisms that generate the privacy paradox in the first place. (shrink)

Africa has a colonial past that renders it a linguistic melting pot, where language is not only important for communication but is inextricably related to cultural identity. In Africa, there are over 2000 languages that are still being used and spoken. Language diversity coupled with cultural diversity may affect the process of obtaining informed consent in data-intensive research. We explore some of the challenges and opportunities of multilingualism in handling informed consent in the context of data-intensive research. In (...) multilingual contexts, as in most African countries, language is exceptionally central, and translation has potential cultural, social, historical, functional and scientific importance. However, it is recognised that terminological and translation activities may not always be cost-effective or feasible. We consider alternative mechanisms of harmonisation of data-related terminology and concepts in multilingual contexts, such as iconography, graphic elicitation and other multimedia formats of information sharing. The inclusion of visual or multimedia explanations in informed consent forms can improve comprehension, enhance information transfer and learning, reduce potential vulnerabilities associated with low literacy levels or the inability to interpret technical language associated with data-intensive research, build trust with participants and their communities, and promote autonomy of potential participants. We recognise that the inclusion of visual or multimedia content to facilitate information transfer is only one component of the informed consent process for data-intensive research. Research ethics committees (RECs) should be mindful of other key considerations and challenges of informed consent for data-intensive research in sub-Saharan Africa (SSA), and to explore whether these alternative forms of consent are ethical and effective in multilingual contexts. (shrink)

This paper discusses what it means and what it takes to integrate data in order to acquire new knowledge about biological entities and processes. Maureen O’Malley and Orkun Soyer have pointed to the scientific work involved in data integration as important and distinct from the work required by other forms of integration, such as methodological and explanatory integration, which have been more successful in captivating the attention of philosophers of science. Here I explore what data integration involves (...) in more detail and with a focus on the role of data-sharing tools, like online databases, in facilitating this process; and I point to the philosophical implications of focusing on data as a unit of analysis. I then analyse three cases of data integration in the field of plant science, each of which highlights a different mode of integration: inter-level integration, which involves data documenting different features of the same species, aims to acquire an interdisciplinary understanding of organisms as complex wholes and is exemplified by research on Arabidopsis thaliana; cross-species integration, which involves data acquired on different species, aims to understand plant biology in all its different manifestations and is exemplified by research on Miscanthus giganteus; and translational integration, which involves data acquired from sources within as well as outside academia, aims at the provision of interventions to improve human health and is exemplified by research on Phytophtora ramorum. Recognising the differences between these efforts sheds light on the dynamics and diverse outcomes of data dissemination and integrative research; and the relations between the social and institutional roles of science, the development of data-sharing infrastructures and the production of scientific knowledge. (shrink)

In this paper, we describe the challenges of collecting data in the Māori population for automatic detection of schizophrenia using natural language processing (NLP). Existing psychometric tools for detecting are wide ranging and do not meet the health needs of indigenous persons considered at risk of developing psychosis and/or schizophrenia. Automated methods using NLP have been developed to detect psychosis and schizophrenia but lack cultural nuance in their designs. Research incorporating the cultural aspects relevant to indigenous communities is lacking (...) in the design of existing automatic prediction tools and one of the main reasons is the scarcity of data from indigenous populations. This paper explores the current design of the New Zealand health care system and its potential impacts on access and inequities in the Māori population and details the methodology used to collect speech samples of Māori at risk of developing psychosis and schizophrenia. The paper also describes the major obstacles faced during speech data collection, key findings, and probable solutions. (shrink)

The paper discusses the extent to which the availability of unprecedentedly rich data-sets and the need for new approaches – both epistemological and computational – is an emerging issue for Macroeconomics. By adopting an evolutionary approach, we describe the paradigm shifts experienced in the macroeconomic research field and emphasize that the types of data the macroeconomist has to deal with play an important role in the evolutionary process of the development of the discipline. After introducing the current debate (...) over Big Data in social sciences, the paper presents a detailed discussion of possible and existing interactions between Big Data and Computational Behavioral Macroeconomics. We argue that Big Data applied to economic questions can lead to new styles of thinking and research methods, namely to the development of a new research paradigm. (shrink)

Publication of pharmaceutical company-sponsored research in medical journals, and its presentation at conferences and meetings, is mostly governed by ‘publication plans’ that extract the maximum amount of scientific and commercial value out of data and analyses through carefully constructed and placed papers. Clinical research is typically performed by contract research organizations, analyzed by company statisticians, written up by independent medical writers, approved and edited by academic researchers who then serve as authors, and the whole process organized and shepherded through (...) to journal publication by publication planners. This paper reports on a conference of an international association of publication planners. It describes and analyzes their work in an ecological framework that relates it to marketing departments of pharmaceutical companies, medical journals and publishers, academic authors, and potential audiences. The medical research described here forms a new kind of corporate science, designed to look like traditional academic work, but performed largely to market products. (shrink)

Bold approaches to data collection and large-scale quantitative advances have long been a preoccupation for social science researchers. In this commentary we further debate over the use of large-scale survey data and official statistics with ‘Big Data’ methodologists, and emphasise the ability of these resources to incorporate the essential social and cultural heredity that is intrinsic to the human sciences. In doing so, we introduce a series of new data-sets that integrate approximately 30 (...) years of survey data on victimisation, fear of crime and disorder and social attitudes with indicators of socio-economic conditions and policy outcomes in Britain. The data-sets that we outline below do not conform to typical conceptions of ‘Big Data’. But, we would contend, they are ‘big’ in terms of the volume, variety and complexity of data which has been collated and ‘big’ also in that they allow us to explore key questions pertaining to how social and economic policy change at the national level alters the attitudes and experiences of citizens. Importantly, they are also ‘small’ in the sense that the task of rendering the data usable, linking it and decoding it, required both manual processing and tacit knowledge of the context of the data and intentions of its creators. (shrink)

It has been argued that ethical frameworks for data science often fail to foster ethical behavior, and they can be difficult to implement due to their vague and ambiguous nature. In order to overcome these limitations of current ethical frameworks, we propose to integrate the analysis of the connections between technical choices and sociocultural factors into the data science process, and show how these connections have consequences for what data subjects can do, accomplish, and be. Using healthcare (...) as an example, attention to sociocultural conversion factors relevant to health can help in navigating technical choices that require broader considerations of the sociotechnical system, such as metric tradeoffs in model validation, resulting in better ethical and technical choices. This approach promotes awareness of the ethical dimension of technical choices by data scientists and others, and that can foster the cultivation of 'ethical skills' as integral to data science. (shrink)

When making a decision, humans consider two types of information: information they have acquired through their prior experience of the world, and further information they gather to support the decision in question. Here, we present evidence that data from search engines such as Google can help us model both sources of information. We show that statistics from search engines on the frequency of content on the Internet can help us estimate the statistical structure of prior experience; and, specifically, we (...) outline how such statistics can inform psychological theories concerning the valuation of human lives, or choices involving delayed outcomes. Turning to information gathering, we show that search query data might help measure human information gathering, and it may predict subsequent decisions. Such data enable us to compare information gathered across nations, where analyses suggest, for example, a greater focus on the future in countries with a higher per capita GDP. We conclude that search engine data constitute a valuable new resource for cognitive scientists, offering a fascinating new tool for understanding the human decision-making process. (shrink)

The analysis of video-recorded interaction consists of various professionalized ways of seeing participant behavior through multimodal, co-operative, or intercorporeal lenses. While these perspectives are often adopted simultaneously, each creates a different view of the human body and interaction. Moreover, microanalysis is often produced through local practices of sense-making that involve the researchers’ bodies. It has not been fully elaborated by previous research how adopting these different ways of seeing human behavior influences both what is seen from a video and how (...) it is seen, as well as the way the interpretation of the data ultimately unfolds in the interaction between researchers. In this article, we provide a theoretical-methodological discussion of the microanalytic research process. We explore how it differs from “seeing” affect in interaction either as a co-operative and multimodal action or as an intercorporeal experience. First, we introduce the multimodal conversation analytic, co-operative, and intercorporeal approaches to microanalysis. Second, we apply and compare these practices to a video-recorded interaction of a romantic couple. Furthermore, we examine a video-recorded episode of us, the researchers, reflecting on our analytic observations about this interaction. We suggest that adopting a multimodal and co-operative perspective constructs affect as co-produced and displayed through observable action, while an intercorporeal perspective produces affect as an embodied and experienced phenomenon. While the former enables locating affect in a specific moment and identifiable body parts, the latter facilitates recognizing the experienced side of affect. These different modes of professional vision complement one another in capturing affect in interaction while being fundamentally used in local interactions between the researchers. (shrink)

The AI and Society discourse has previously drawn attention to the ways that digital systems embody the values of the technology development community from which they emerge through the development and deployment process. Research shows how this effect leads to a particular treatment of gender in computer systems development, a treatment which lags far behind the rich understanding of gender that social studies scholarship reveals and people across society experience. Many people do not relate to the narrow binary gender (...) options of male or female, and many people express their gender identity in much richer ways than the sex/gender binary female/woman and male/man Boolean terms will allow. We ask: are “born-digital” gendered datasets in digital systems experienced as marginalising by those who express their identity beyond the male/female binary? Case Study: Ireland. To answer this universal question, this paper presents the findings of an empirical case study of people in Ireland with diverse gender identities and expressions, and their experiences with public data systems and new technologies. In spite of great social changes in Ireland which have led to constitutional change in favour of LGBTQI + people, born-digital systems were experienced by respondents as embodying socio-cultural values which were no longer accepted in society at large. For many of the respondents, digital technologies routinely marginalise them in all kinds of ways. These systems keep alive violence and oppression long after civil rights have been enshrined in constitutional law. This study is just one example of the way assumptions about digital are disengaged from society-at-large. It is a call to arms to all who are passionate about socially-responsible technology. (shrink)

What could a social-justice oriented, feminist data studies look like? The current datalogical turn foregrounds the digital datafication of everyday life, increasing algorithmic processing and data as an emergent regime of power/knowledge. Scholars celebrate the politics of big data knowledge production for its omnipotent objectivity or dismiss it outright as data fundamentalism that may lead to methodological genocide. In this feminist and postcolonial intervention into gender-, race- and geography-blind ‘big data’ ideologies, I call for (...) ethical, anti-oppressive digital data-driven research in the social sciences and humanities. I argue that a reflexive data scholarship can emerge from the reintegration of feminist and postcolonial science studies and ethics of care ideals. Although it is not a panacea for all ails of data mining, I offer a road map for an alternative data-analysis practice that is more power-sensitive and accountable. By incorporating a people-centric and context-aware perspective that acknowledges relationships of dependency, reflects on temptations, and scrutinises benefits and harm, an ‘asymmetrically reciprocal’ (Young, 1997) research encounter may be achieved. I bring this perspective to bear on experiences of a two-year research project with eighty-four young Londoners on digital identities and living in a highly diverse city. I align awareness of uneven relations of power and knowledge with the messy relation of dependency between human and non-human actors in data analysis. This framework productively recognises that digital data cannot be expected to speak for itself, that data do not emerge from a vacuum, and that isolated data patterns cannot be the end-goal of a situated and reflexive research endeavor. Data-driven research, in turn, shows the urgency for renewed feminist ethical reflection on how digital mediation impacts upon responsibility, intersectional power relations, human subjectivity and the autonomy of research participants over their own data. (shrink)

Cognitive scientists have focused too narrowly on the acquisition of data and on the methods to extract patterns from those data. We argue that a successful science of the mind requires widening our focus to include the problems being solved by cognitive processes. Frameworks that characterize cognitive processes in terms of instrumental problem‐solving, such as those within the evolutionary social sciences, become necessary if we wish to discover more accurate descriptions of those processes.

Considering the technological development today, we are facing an emerging crisis. We are in the midst of a scientific revolution, which promises to radically change not only the way we live and work—but beyond that challenge the stability of the very foundations of our civilization and the international political order. All our attention and effort is thus focused on cushioning its impacts on life and society. Looking back in history, it would be pertinent to ask whether this process is a (...) “quasi” natural event destined to continue its path which forebodes severe ethical, social and political repercussions, or is it the outcome of a particular socio-cultural value system? What then are the different value systems and mind-sets? How do these impact on our perception of the world, our cognition systems and our self-perception? At the core of this revolution are the recent technological developments in information and data processing and the creation of AI and the role of algorithms. Are algorithms quasi “independent” artificial languages for assessing situations, solving problems and taking decisions on fundamental social issues? Or do they rest on the fundament of natural language? What ethical and moral code do they follow? What drivers underlie this technological process? Are these culture specific or universal? Could a change of “cultural perspective” provide us with new insights to steer the process and change its course? (shrink)

An accessible and contemporary introduction to the methods for determining cause and effect in the social sciences Causal inference encompasses the tools that allow social scientists to determine what causes what. Economists--who generally can't run controlled experiments to test and validate their hypotheses--apply these tools to observational data to make connections. In a messy world, causal inference is what helps establish the causes and effects of the actions being studied, whether the impact (or lack thereof) of (...) increases in the minimum wage on employment, the effects of early childhood education on incarceration later in life, or the introduction of malaria nets in developing regions on economic growth. Scott Cunningham introduces students and practitioners to the methods necessary to arrive at meaningful answers to the questions of causation, using a range of modeling techniques and coding instructions for both the R and Stata programming languages. - -. (shrink)

In response to calls for greater interdisciplinary involvement from the social sciences and humanities in the development, governance, and study of artificial intelligence systems, this paper presents one sociologist’s view on the problem of algorithmic bias and the reproduction of societal bias. Discussions of bias in AI cover much of the same conceptual terrain that sociologists studying inequality have long understood using more specific terms and theories. Concerns over reproducing societal bias should be informed by an understanding of (...) the ways that inequality is continually reproduced in society—processes that AI systems are either complicit in, or can be designed to disrupt and counter. The contrast presented here is between conservative and radical approaches to AI, with conservatism referring to dominant tendencies that reproduce and strengthen the status quo, while radical approaches work to disrupt systemic forms of inequality. The limitations of a conservative approach to racial bias are discussed through the specific example of biased criminal risk assessments and Indigenous overrepresentation in Canada’s criminal justice system. This illustrates the dangers of treating racial bias as a generalizable problem and equality as a generalizable solution, emphasizing the importance of considering inequality in context. Societal issues can no longer be out of scope for AI and machine learning, given the impact of these systems on human lives. This requires engagement with a growing body of critical AI scholarship that goes beyond biased data to analyze structured ways of perpetuating inequality, opening up the possibility for interdisciplinary engagement and radical alternatives. (shrink)

Measurement is any process by which a value is assigned to the level or state of some quality of an object of study. This value is given numerical form, and measurement therefore involves the expression of information in quantities rather than by verbal statement. It provides a powerful means of reducing qualitative data to more condensed form for summarization, manipulation and analysis. The classical distinctions made by S S S Stevens between nominal, ordinal, interval and ratio measurement are a (...) common starting point for discussion of social science measurement and the use of statistical techniques. This article considers the applicability of this model to research in the social sciences, particularly sociology and political science. Differences between classical measurement theory and practice in the social sciences are discussed. Problems in social science conceptualization, operationalization and measurement are examined, and a number of issues raised. Three cases are given particular attention, the Social Indicators movement, the variable of social class, and measures of race and ethnicity. The article concludes that the 'ineluctable fuzziness' of many social science concepts is inescapable, and that social measurement remains a difficult and challenging issue. (shrink)

Recent years have seen a surge in online collaboration between experts and amateurs on scientific research. In this article, we analyse the epistemological implications of these crowdsourced projects, with a focus on Zooniverse, the world’s largest citizen science web portal. We use quantitative methods to evaluate the platform’s success in producing large volumes of observation statements and high impact scientific discoveries relative to more conventional means of data processing. Through empirical evidence, Bayesian reasoning, and conceptual analysis, we show how (...) information and communication technologies enhance the reliability, scalability, and connectivity of crowdsourced e-research, giving online citizen science projects powerful epistemic advantages over more traditional modes of scientific investigation. These results highlight the essential role played by technologically mediated social interaction in contemporary knowledge production. We conclude by calling for an explicitly sociotechnical turn in the philosophy of science that combines insights from statistics and logic to analyse the latest developments in scientific research. (shrink)