Data Collection Practices Research Articles

Addressing critical knowledge and capacity gaps to sustain CRVS system development

BackgroundImproving civil registration and vital statistics (CRVS) systems requires strengthening the capacity of the CRVS workforce. The improvement of data collection and diagnostic practices must be accompanied by efforts to ensure that the workforce has the skills and knowledge to assess the quality of, and analyse, CRVS data using demographic and epidemiological techniques. While longer-term measures to improve data collection practices must continue to be implemented, it is important to build capacity in the cautious use of imperfect data. However, a lack of training programmes, guidelines and tools make capacity shortages a common issue in CRVS systems. As such, any strategy to build capacity should be underpinned by (1) a repository of knowledge and body of evidence on CRVS, and (2) targeted strategies to train the CRVS workforce.Main textDuring the 4 years of the Bloomberg Philanthropies Data for Health (D4H) Initiative at the University of Melbourne, an extensive repository of knowledge and practical tools to support CRVS system improvements was developed for use by various audiences and stakeholders (the ‘CRVS Knowledge Gateway’). Complementing this has been a targeted strategy to build CRVS capacity in countries that comprised two approaches – in-country or regional training and a visiting Fellowship Program. These approaches address the need to build competence in countries to collect, analyse and effectively use good quality birth and death data, and a longer-term need to ensure that local staff in countries possess the comprehensive knowledge of CRVS strategies and practices necessary to ensure sustainable CRVS development.ConclusionThe Knowledge Gateway is a dynamic, useful and long-lasting repository of CRVS knowledge for countries and development partners to use to formulate and evaluate CRVS development strategies. Capacity-building through in-country or regional training and the University of Melbourne D4H Fellowship Program will ensure that CRVS capacity and knowledge is developed and maintained, facilitating improvements in CRVS data systems that can be used by policymakers to support better decision-making in health.

Patterns of heritability across the human diphyodont dental complex: Crown morphology of Australian twins and families.

This study generates a series of narrow-sense heritability estimates for crown morphology of the deciduous and permanent dentition with two overarching aims. The first is to test the hypothesis that deciduous teeth provide a more faithful reflection of genetic information than their permanent successors. The second is to use quantitative genetic methods to evaluate assumptions underlying common data collection and analysis practices in biodistance research. Dental morphology data were collected from longitudinal dental casts representing Australian twins and families using Arizona State Dental Anthropology System standards. Polygenic models and estimates of narrow-sense heritability were generated using SOLAR v.8.1.1. Each model considered age, sex, and age/sex interaction as covariates. Heritability estimates significantly differed from zero for the majority of morphological crown characters. Most estimates fell within the 0.4-0.8 range typically observed for crown morphology. Mean heritability was stable across the dental complex, but for paired homologues, permanent traits often yielded higher estimates than their deciduous counterparts. Results indicate directional asymmetry in environmental influence for crown morphology and inform biodistance "best practices" related to data collection and treatment. Overall, results for this sample support the use of crown morphology as a proxy for genetic variation in evolutionary research. This includes the deciduous dentition, which justifies the expansion of efforts to incorporate subadults into reconstructions of past microevolutionary processes. Results do not indicate that deciduous phenotypes more closely approximate underlying genotype, at least for deciduous/permanent homologues.

An antenatal alcohol service evaluation of the north-east of England and north Cumbria

NHS England's 'Better Births' strategy aims to improve maternal and infant health outcomes. A strategic priority identified in the north-east local maternity system is to reduce alcohol consumption in pregnancy due to the documented diverse risks of harm to mother and baby, including foetal alcohol spectrum disorder. To evaluate current alcohol prevention, screening and treatment service provision in maternity care across the region, and inform future recommendations. A service evaluation survey was developed to systematically consult strategic stakeholders across all nine maternity trusts in the region over a 2-month period in 2018. Content analysis was employed to identify fundamental themes and inform recommendations for practice. High variation was reported throughout regional clinical practices, service provision and staff training. For example, a number of alcohol screening tools were identified, each with diverse thresholds for referral; reported data collection and documentation practices were multifarious, incomparable and unquantifiable; audit was rare and guidelines were primarily influenced by local commissioning agreements. Standardized patient pathways involving alcohol screening and management practices are required, and sharing best practices will facilitate referrals and support regardless of location. The implementation of these recommendations requires appropriate leadership, commissioning and training strategies.

Variability in Collection and Use of Race/Ethnicity and Language Data in 93 Pediatric Hospitals

To describe how pediatric hospitals across the USA and Canada collect race/ethnicity and language preference (REaL) data and how they stratify quality and safety metrics using such data. Pediatric hospitals from the Solutions for Patient Safety network (125 US, 6 Canadian) were surveyed between January and March 2018 on collection and use of patient/family race/ethnicity data and patient/family language preference data. The study team created the survey using a formal process including pre-testing. Responses were analyzed using descriptive statistics. Ninety-three of 131 (71%) hospitals completed the survey (87/125 [70%] US, 6/6 [100%] Canadian). Patient race/ethnicity was collected by 95%, parent/guardian race/ethnicity was collected by 31%, and 5/6 Canadian hospitals collected neither. Minimum government race/ethnicity categories were used without modification/addition by 68% of US hospitals. Eleven hospitals (13%) offered a multiracial/multiethnic option. Most hospitals reported collecting language preferences of parent/guardian (81%) and/or patient (87%). A majority provided formal training on data collection for race/ethnicity (70%) and language preferences (70%); fewer had a written policy (41%, 51%). Few hospitals stratified hospital quality and safety measures by race/ethnicity (20% readmissions, 20% patient/family experience, 16% other) or language preference (21% readmissions, 21% patient/family experience, 8% other). The variability of REaL data collection practices among pediatric hospitals highlights the importance of examining the validity and reliability of such data, especially when combined from multiple hospitals. Nevertheless, while improvements in data accuracy and standardization are sought, efforts to identify and eliminate disparities should be developed concurrently using existing data.

An integrated UGV-UAV system for construction site data collection

There have been recent efforts to increase the degree of automation and frequency of data collection for construction applications using Unmanned Aerial/Ground Vehicles (UAV/UGV). However, the current practice of data collection is traditionally performed, which is manual, costly, time-consuming, and error-prone. Developing vision-based mobile robotic systems that are aware of its surrounding and capable of autonomous navigation are becoming essential to many construction applications, namely surveying, monitoring, and inspection. Nevertheless, the systems above suffer from a series of performance issues. One major problem is inefficient navigation in indoor and cluttered scenes with many obstacles and barriers, where some places are inaccessible by a UGV. To provide a solution to this problem, this paper designs a UAV-UGV team that integrates two custom-built mobile robots. The UGV autonomously navigates through space, leveraging its sensors. The UAV acts as an external eye for the UGV, observing the scene from a vantage point that is inaccessible to the UGV. The relative pose of the UAV is estimated continuously, which allows it to maintain a fixed location that is relative to the UGV. The key aspects for the development of this system that is capable of autonomous navigation are the localization of both UAV and UGV, mapping of the surrounding environment, and efficient path planning using multiple sensors. The proposed system is tested in an indoor and cluttered construction-like environment. The performance of the system demonstrates the feasibility of developing and deploying a robust and automated data collection system for construction applications in the near future.

Jointly Inferring the Dynamics of Population Size and Sampling Intensity from Molecular Sequences.

Estimating past population dynamics from molecular sequences that have been sampled longitudinally through time is an important problem in infectious disease epidemiology, molecular ecology, and macroevolution. Popular solutions, such as the skyline and skygrid methods, infer past effective population sizes from the coalescent event times of phylogenies reconstructed from sampled sequences but assume that sequence sampling times are uninformative about population size changes. Recent work has started to question this assumption by exploring how sampling time information can aid coalescent inference. Here, we develop, investigate, and implement a new skyline method, termed the epoch sampling skyline plot (ESP), to jointly estimate the dynamics of population size and sampling rate through time. The ESP is inspired by real-world data collection practices and comprises a flexible model in which the sequence sampling rate is proportional to the population size within an epoch but can change discontinuously between epochs. We show that the ESP is accurate under several realistic sampling protocols and we prove analytically that it can at least double the best precision achievable by standard approaches. We generalize the ESP to incorporate phylogenetic uncertainty in a new Bayesian package (BESP) in BEAST2. We re-examine two well-studied empirical data sets from virus epidemiology and molecular evolution and find that the BESP improves upon previous coalescent estimators and generates new, biologically useful insights into the sampling protocols underpinning these data sets. Sequence sampling times provide a rich source of information for coalescent inference that will become increasingly important as sequence collection intensifies and becomes more formalized.

On the need for expanded guidance in navigating ethical learning research at science museums

AbstractScience learning occurs throughout people's lives, inside and outside of school, in formal, informal, and nonformal settings. While museums have long played a role in science education, learning in this and other informal settings has not been studied nor understood as deeply as in formal settings (i.e., schools and classrooms). This position paper, written by learning researchers in a science museum engaged in equity and access work, notes that while the researchers consider the ethics of their work regularly and deeply, little formal guidance exists for the ethical challenges they routinely face in studying science learning. To explore this, the paper first shares contexts of studying informal science education at the Science Museum of Minnesota, including epistemological understandings of both science and research, a commitment to justice‐based equity, and existing ethical guidance and processes. Drawing on three research projects, it explores ethical issues pertaining to (a) museum visitors and (b) museum staff and community members engaging in participatory research. First, as visitors do not generally come to a museum to be part of a research study, learning researchers must consider sampling, representation, and data collection methods, balancing these with a museum‐goer's desires for their visit. Second, when using participatory methodologies with staff, community members, and young people as co‐researchers, ethical considerations involve building relationships, redefining (unanticipated) risks, and data collection and dissemination practices that do not extend existing social inequities or work hierarchies. Ultimately, this position paper argues for expanded or revised ethical guidance that meets the needs of this work, surpasses current guidelines or institutional review board practices, draws on epistemologies outside of a supposedly neutral, individualistic Western framework, and places participants at the center of the work. Such a discussion could enhance the ethical study of science learning across settings.

The Economics of the German Investigation of Facebook’s Data Collection

The importance of digital platforms and related data-driven business models is ever in-creasing and poses challenges for the workability of competition in the respective markets (tendencies towards dominant platforms, paying-with-data instead of traditional money, privacy concerns, etc.). Due to such challenges, investigations of such markets are of high interest. One of recent cases is the investigation of Facebook’s data collection practices by German competition authorities. Our paper, in contrast to the wide stream of legal stud-ies on this case, aims to analyze whether Facebook’s practices regarding data collection could constitute an abuse of market power from an economic perspective, more specifi-cally against the background of modern data economics. In doing so we summarize the state of the advanced theories, including influences from behavioral economics, address-ing such markets, and discuss four potential theories of harm.

Antitrust in Attention Markets

Despite its vital role in the modern marketplace, attention has largely escaped the notice of the antitrust community. Existing discourse exhibits a variety of misconceptions and flawed prescriptions. One example is the widely held notion that attention markets necessitate two-sided platform analysis. Another is a marked overemphasis on data-collection practices. Yet another is that human users, viewers, and listeners are “the consumers” in these markets. And a fourth is that competition for attention occurs within one massive relevant market, obviating the possibility that any single firm could exercise market power. In light of nascent enforcement actions against Facebook, Google, and others, these defects require immediate correction. This Article begins by explaining the basic economics of attention markets, which often involve zero-price barter transactions. It turns next to the appropriate antitrust methodology for market definition. Attention markets need not encompass two “sides”; instead, they are best understood as traditional top-down distribution systems. The oft-used SSNIP test is facially unworkable in zero-price attention markets, but a SSNIC or SSNDQ variant may offer some utility. Practical indicia will often be more useful, however, given the unwieldy nature of hypothetical-monopolist tests generally. Regardless of methodology, courts and enforcers should take care to avoid the “massive market” fallacy espoused by some commentators. When assessing market power, both market shares and direct evidence can be useful. The three most common methods for assigning shares—time on-site, active users, and advertising revenues—each shed light on the issue, despite early judicial skepticism. That said, wherever available, direct evidence regarding attention-cost changes and competitive responses should play a primary role. As to anticompetitive effects, the “attention overcharge” should be a core concern. Recent enforcement efforts have employed alternative framing devices, but these will often be suboptimal. Finally, competition for attention can lead to overuse, overconsumption, heightened racial and gender animus, and other societal ills. In response, courts and enforcement authorities should extend leniency to certain attention-related conduct that might initially appear harmful.

Collaborative Assessment of an Academic Library and Writing Center Partnership: Embedded Writing and Research Tutors for First-Year Students

This article reports on collaborative research that assessed the impact of a joint library and writing center initiative: the Writing and Research Tutor program. This program provided an integrated model of academic support to underrepresented first-year students who were paired with a tutor trained in both writing and research support. The St. Olaf Libraries and the Center for Advising and Academic Support partnered with the Office of Institutional Effectiveness and Assessment to ask: How can our services—and our data collection and assessment practices—align to foster student success? How can we collaboratively contribute to institutional efforts to improve student success? In answering these questions, we chose a small-scale pilot program and created mixed-methods assessments including self-efficacy surveys, written reflections, focus groups, and interviews with former tutors. The assessments provided opportunities for student reflection and metacognition and explicitly connected the results of our study to both national and local measures of success.

Resurfacing Historical Scientific Data: A Case Study Involving Fruit Breeding Data

<strong>Objective</strong>: The objective of this paper is to illustrate the importance and complexities of working with historical analog data that exists on university campuses. Using a case study of fruit breeding data, we highlight issues and opportunities for librarians to help preserve and increase access to potentially valuable data sets. <strong>Methods</strong>: We worked in conjunction with researchers to inventory, describe, and increase access to a large, 100-year-old data set of analog fruit breeding data. This involved creating a spreadsheet to capture metadata about each data set, identifying data sets at risk for loss, and digitizing select items for deposit in our institutional repository. <strong>Results/Discussion</strong>: We illustrate that large amounts of data exist within biological and agricultural sciences departments and labs, and how past practices of data collection, record keeping, storage, and management have hindered data reuse. We demonstrate that librarians have a role in collaborating with researchers and providing direction in how to preserve analog data and make it available for reuse. This work may provide guidance for other science librarians pursing similar projects. <strong>Conclusions</strong>: This case study demonstrates how science librarians can build or strengthen their role in managing and providing access to analog data by combining their data management skills with researchers’ needs to recover and reuse data. <em>The substance of this article is based upon a panel presentation at RDAP Summit 2019.</em>

Temporal changes in distributions and the species atlas: How can British and Irish plant data shoulder the inferential burden?

Species distribution atlases often rely on volunteer effort to achieve their desired coverage, an activity now typically discussed, at least in academia, under the general theme of “citizen science”. Such data, however, are rarely without complex biases, particularly with respect to the estimation of trends in species’ distributions over many decades. The data of the Botanical Society of Britain and Ireland (BSBI) are no exception to this, and both careful thought in data aggregation (spatial, temporal, and taxonomic) and appropriate modelling procedures are required to overcome these challenges. We discuss these issues, with a primary focus on the statistical models that have been put forward to adjust for such biases. Such models include the Telfer method, various “reporting rate” approaches based on generalised linear models, the frequency scaling using local occupancy (“Frescalo”) model, occupancy models, and spatial smoothing methods. In each case the strengths and limitations in relation to estimating trends from distribution data with important time-varying biases are assessed. Various properties of BSBI data, in particular the increasing numbers of records at fine spatial and temporal scales over the past century, coupled with a general lack of re-visits to sites at such finer scales and the time-varying biases previously mentioned, imply that methods that can be sensibly applied at coarser levels are likely to be most appropriate for estimating accurate long-term trends in distributions. We conclude that Frescalo, which can be seen as a type of occupancy model where an adjustment for overlooked species is made in relation to spatial rather than temporal replication, whilst simultaneously adjusting for variable regional effort, is currently the most sophisticated tool for achieving this. Although recording community-accepted adjustments to data collection practices may allow for a greater application of occupancy modelling or other approaches in the future, methods that seek accurate trends over the long-term are necessarily limited either to scales at which various properties of the data in hand are most likely to be unbiased, or at which the biases are well enough understood to be modelled accurately.

A Question of Time: HQ Trivia and Mobile Streaming Temporality

A Question of Time: HQ Trivia and Mobile Streaming Temporality

Producing routine malaria data: an exploration of the micro-practices and processes shaping routine malaria data quality in frontline health facilities in Kenya

BackgroundRoutine health information systems can provide near real-time data for malaria programme management, monitoring and evaluation, and surveillance. There are widespread concerns about the quality of the malaria data generated through routine information systems in many low-income countries. However, there has been little careful examination of micro-level practices of data collection which are central to the production of routine malaria data.MethodsDrawing on fieldwork conducted in two malaria endemic sub-counties in Kenya, this study examined the processes and practices that shape routine malaria data generation at frontline health facilities. The study employed ethnographic methods—including observations, records review, and interviews—over 18-months in four frontline health facilities and two sub-county health records offices. Data were analysed using a thematic analysis approach.ResultsMalaria data generation was influenced by a range of factors including human resource shortages, tool design, and stock-out of data collection tools. Most of the challenges encountered by health workers in routine malaria data generation had their roots in wider system issues and at the national level where the framing of indicators and development of data collection tools takes place. In response to these challenges, health workers adopted various coping mechanisms such as informal task shifting and use of improvised tools. While these initiatives sustained the data collection process, they also had considerable implications for the data recorded and led to discrepancies in data that were recorded in primary registers. These discrepancies were concealed in aggregated monthly reports that were subsequently entered into the District Health Information Software 2.ConclusionChallenges to routine malaria data generation at frontline health facilities are not malaria or health information systems specific; they reflect wider health system weaknesses. Any interventions seeking to improve routine malaria data generation must look beyond just malaria or health information system initiatives and include consideration of the broader contextual factors that shape malaria data generation.

Health system readiness to support facilities for care of preterm, low birth weight, and sick newborns in Ethiopia: a qualitative assessment

BackgroundPreterm birth is a worldwide challenge with the highest burden in low- and middle-income countries. Despite availability of low-cost interventions to decrease mortality of preterm, low birth weight, and sick newborns, these interventions are not well integrated in the health systems of low- and middle-income countries. The aim of this study was to assess, from the perspective of key stakeholders comprising leaders in the public health system, the health system readiness to support health care facilities in the care provided to preterm, low birth weight, and sick newborns in different regions of Ethiopia.MethodsA qualitative assessment using in-depth interviews with health facility leaders was conducted in health facilities in 3 regions of Ethiopia from December 2017 to February 2018. The interview guide was developed using a modified version of the World Health Organization health system building blocks.ResultsAcross the public health system, adequate and reliable space, power, and water were problematic. Human resource issues (training, staffing, and retention) were critical to being able to properly care for preterm, low birth weight, and sick newborns. Problems with functional equipment and equipment distribution systems were widespread. Funds were lacking to support preterm, low birth weight, and sick newborn needs in facilities. Data collection practices, data quality, and data utilization were all problematic. There were gaps in the availability of guidelines and protocols, specifically targeting preterm, low birth weight, and sick newborn care. Key facilitators, information disseminators, and influencers identified in the study were the Health Development Army, community and religious leaders, and mothers and families who had had positive experiences or outcomes of care.ConclusionsThe Ethiopian health system has opportunities across all 7 World Health Organization health system building blocks to strengthen readiness to support health facilities to provide quality care and improve outcomes for preterm, low birth weight, and sick newborns.

Development and challenges in setting up an international bone infection registry.

Osteomyelitis is an increasing burden on the society especially due to the emergence of multiple drug-resistant organisms. The lack of a central registry that prospectively collects data on patient risk factors, laboratory test results, treatment modalities, serological analysis results, and outcomes has hampered the research effort that could have improved and provided guidelines for treatments of bone infections. The current manuscript describes the lessons learned in setting up a multi-continent registry. This multicenter, international registry was conducted to prospectively collect essential patient, clinical, and surgical data with a 1-year follow-up period. Patients 18years or older with confirmed S. aureus long bone infection through fracture fixation or arthroplasty who consented to participate in the study were included. The outcomes using the Short Form 36 Health Survey Questionnaire (version 2), Parker Mobility Score, and Katz Index of Independence in Activities of Daily Living were assessed at baseline and at 1month, 6months, and 12months. Serological samples were collected at follow-ups. Contract negotiation with a large number of study sites was difficult; obtaining ethics approvals were time-consuming but straightforward. The initial patient recruitment was slow, leading to a reduction of target patient number from 400 to 300 and extension of enrollment period. Finally, 292 eligible patients were recruited by 18 study sites (in 10 countries of 4 continents, Asia, North and South America, and Central Europe). Logistical and language barriers were overcome by employing courier service and local monitoring personnel. Multicenter registry is useful for collecting a large number of cases for analysis. A well-defined data collection practice is important for data quality but challenging to coordinate with the large number of study sites.

Impact of Internal Migration on Population Redistribution in Europe: Urbanisation, Counterurbanisation or Spatial Equilibrium?

The classical foundations of migration research date from the 1880s with Ravenstein’s “Laws of migration”, which represent the first comparative analyses of internal migration. While his observations remain largely valid, the ensuing century has seen considerable progress in data collection practices and methods of analysis, which in turn has permitted theoretical advances in understanding the role of migration in population redistribution. Coupling the extensive range of migration data now available with these recent theoretical and methodological advances, we endeavour to advance beyond Ravenstein’s understanding by examining the direction of population redistribution and comparing the impact of internal migration on patterns of human settlement in 27 European countries. Results show that the overall redistributive impact of internal migration is low in most European countries but the mechanisms differ across the continent. In Southern and Eastern Europe migration effectiveness is above average but is offset by low migration intensities, whereas in Northern and Western Europe high intensities are absorbed in reciprocal flows resulting in low migration effectiveness. About half the European countries are experiencing a process of concentration toward urbanised regions, particularly in Northern, Central and Eastern Europe, whereas countries in the West and South are undergoing a process of population deconcentration. These results suggest that population deconcentration is now more common than it was in the 1990s when counterurbanisation was limited to Western Europe. The results show that 130 years on, Ravenstein’s law of migration streams and counter-streams remains a central facet of migration dynamics, while underlining the importance of simple yet robust indices for the spatial analysis of migration.* This article belongs to a special issue on “Internal Migration as a Driver of Regional Population Change in Europe: Updating Ravenstein”.

Modelling non-response in establishment-based freight surveys: A sampling tool for statewide freight data collection in middle-income countries

Non-response is unavoidable in any disaggregate-level survey efforts where information is sought from individuals. There is a strong need to understand the contributing factors of non-response behavior in freight surveys as there is very little information on what sort of establishments respond positively to survey inquiries. To meet this research gap, this paper analyzes the non-response behavior in an establishment-based freight survey (EBFS) conducted in Kerala, India. The analysis involves usage of five prominent classifiers: classification trees, random forests, Naïve Bayes classifier, logistic regression and K nearest neighbors. A closer look at the results revealed that the industrial classes handling commodities with high value density (machinery, electronics and electrical equipment, other manufacturing products, textiles) exhibit high non-response probability for freight surveys. This may be explained in terms of the limited logistic information shared by establishments handling high valued products due to less attention given to logistics strategies. Another reason could be that the amount of capital and opportunity costs tied up with these commodities are high, and therefore, their information tend to be more proprietary in nature. The analysis also reveals that the location of establishment plays a modest, yet, noticeable effect on non-response behavior. The establishments from cities located closer to Ports with high per-capita income are less likely to respond to survey requests, possibly due to the higher opportunity cost for time and increased concerns of intrusion into privacy. Finally, the comparison of ROC curves suggests that KNN algorithm is most suitable for modelling non-response behavior. These models are expected to be useful for developing sample weighting schemes and targeted incentive strategies that can improve the state of practice of freight data collection in middle-income countries like India. The study findings are expected to be useful for developing more elaborate and dynamically responsive survey designs for EBFS, in which sample recruitment strategies can be adapted real time during data collection. This study also informs policymakers that the apparent trends in non-response, if not arrested, are likely to weaken the validity of inferences drawn from estimates based on EBFS and undermine, perhaps fatally, the potential of EBFS to guide facility planning and policy interventions.

A NEW STANDARD OF PROOF? DISCOURSES ON VISUAL DATA AFTER THE 2017 G20-PROTESTS

A broad body of literature has described contemporary societies as “surveillance societies” or “surveillance cultures” and has expanded on the implications of an increasing “datafication” of society. To date, little attention has been paid to the role of visual data and their analysis in these processes. However, visual data and advancing algorithmic and facial recognition tools can provide particularly rich insights, that may imply both, important potentials and possible tensions. The contribution uses the case of controversial police investigations after the 2017 G20-summit to discuss intersections of datafication, dataveillance and visual communication and to provide insight into how different authorities and stakeholders legitimate and contest the collection of visual data and their algorithmic analysis in the political and public realm. Therefore a qualitative content and discourse analysis of news media articles, tweets, experts’ reports, police statements and minutes of parliamentary debates and committee hearings was conducted. Findings indicate that concrete practices of visual data collection and analysis remained obscure and a critical blind spot in the general media coverage. In turn, they triggered heated debates in the political realm and in specialized media coverage in which trust played a striking key role. Police authorities characterized visual data and algorithmic tools as a "new standard of proof" and thus as particularly powerful, objective and specifically trustworthy. However, indiscriminate practices of visual data collection and analysis also triggered fundamental concerns about the role and the trustworthiness of police authorities in datafied societies.

Going against the (Appropriate) Flow: A Contextual Integrity Approach to Privacy Policy Analysis

We present a method for analyzing privacy policies using the framework of contextual integrity (CI). This method allows for the systematized detection of issues with privacy policy statements that hinder readers’ ability to understand and evaluate company data collection practices. These issues include missing contextual details, vague language, and overwhelming possible interpretations of described information transfers. We demonstrate this method in two different settings. First, we compare versions of Facebook’s privacy policy from before and after the Cambridge Analytica scandal. Our analysis indicates that the updated policy still contains fundamental ambiguities that limit readers’ comprehension of Facebook’s data collection practices. Second, we successfully crowdsourced CI annotations of 48 excerpts of privacy policies from 17 companies with 141 crowdworkers. This indicates that regular users are able to reliably identify contextual information in privacy policy statements and that crowdsourcing can help scale our CI analysis method to a larger number of privacy policy statements.