Researchers have documented the power of legal officials to administer sanctions, from arrest to court surveillance and incarceration. How do those subject to punishment interact with officials and ...attempt to subvert their power? Drawing on interviews and ethnographic observations among 63 criminal defendants and 42 legal officials in the Boston-area court system, this article considers how socioeconomically and racially disadvantaged defendants interact with their defense attorneys, and with what consequences. Given racialized and classed constraints, many disadvantaged defendants mistrust their court-appointed lawyers. Their mistrust often results in withdrawal from their lawyers and active efforts to cultivate their own legal knowledge and skills. Defendants use their lay legal expertise to work around and resist the authority of their lawyers. Defense attorneys and judges respond with silencing and coercion, given the unwritten norms and rules of the court. These findings complicate existing accounts of disadvantaged defendants as passive actors and contribute to cultural sociological and relational theories of how people engage with professionals across institutional spaces. Unlike in mainstream institutions such as schools and hospitals where self-advocacy is rewarded in interactions, criminal court officials reject disadvantaged defendants’ attempts to advocate for themselves.
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As replications of individual studies are resource intensive, techniques for predicting the replicability are required. We introduce the repliCATS (Collaborative Assessments for Trustworthy Science) ...process, a new method for eliciting expert predictions about the replicability of research. This process is a structured expert elicitation approach based on a modified Delphi technique applied to the evaluation of research claims in social and behavioural sciences. The utility of processes to predict replicability is their capacity to test scientific claims without the costs of full replication. Experimental data supports the validity of this process, with a validation study producing a classification accuracy of 84% and an Area Under the Curve of 0.94, meeting or exceeding the accuracy of other techniques used to predict replicability. The repliCATS process provides other benefits. It is highly scalable, able to be deployed for both rapid assessment of small numbers of claims, and assessment of high volumes of claims over an extended period through an online elicitation platform, having been used to assess 3000 research claims over an 18 month period. It is available to be implemented in a range of ways and we describe one such implementation. An important advantage of the repliCATS process is that it collects qualitative data that has the potential to provide insight in understanding the limits of generalizability of scientific claims. The primary limitation of the repliCATS process is its reliance on human-derived predictions with consequent costs in terms of participant fatigue although careful design can minimise these costs. The repliCATS process has potential applications in alternative peer review and in the allocation of effort for replication studies.
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Reliable measurements are key to social science research. Multiple measures of reliability of the total score have been developed, including coefficient alpha, coefficient omega, the greatest lower ...bound reliability, and others. Among these, the coefficient alpha has been most widely used, and it is reported in nearly every study involving the measure of a construct through multiple items in social and behavioral research. However, it is known that coefficient alpha underestimates the true reliability unless the items are tau-equivalent, and coefficient omega is deemed as a practical alternative to coefficient alpha in estimating measurement reliability of the total score. However, many researchers noticed that the difference between alpha and omega is minor in applications. Since the observed differences in alpha and omega can be due to sampling errors, the purpose of the present study, therefore, is to propose a method to evaluate the difference of coefficient alpha (
α
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) and omega (
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) statistically. In particular, the current article develops a procedure to estimate the SE of (
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−
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) and consequently the confidence interval (CI) for (
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). This procedure allows us to test whether the observed difference (
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) is due to sample error or
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is significantly greater than
α
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. The developed procedure is then applied to multiple real data sets from well-known scales to empirically verify the values of (
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) in practice. Results showed that in most of the comparisons the differences are significantly above zero but cases also exist where the CIs contain zero. An R program for calculating
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,
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, and the SE of (
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) is also included in the present study so that the developed procedure is easily accessible to applied researchers.
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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.
A robust literature has shown that surveillance disproportionately targets poor people of color through the criminal justice and welfare systems. However, little empirical research traces the ...mechanisms through which surveillance reproduces inequality in other domains, such as subsidized housing, where private actors including property owners and landlords do the work of surveilling tenants. In this article, I apply the theoretical lens of surveillance to the case of subsidized housing to explore the symbolic and material consequences of being monitored at home. Drawing on in-depth interviews with 67 low-income Black mothers in the Sunnyside neighborhood in Houston, Texas, I argue that the scrutiny mothers face in and around their homes reproduces inequality through two key mechanisms. First, surveillance creates a home environment devoid of privacy, that mothers liken to being in prison. Mothers interpret this scrutiny as an effort to control and contain them because of their race, reinforcing racialized notions of presumptive Black criminality. Second, surveillance heightens the material risk for mothers of being caught breaking rules, which paves the way for eventual eviction and exacerbates poverty. Although mothers develop strategies to counter and at times resist disciplinary monitoring, these efforts come with drawbacks that can make surviving poverty harder. Taken together, these findings suggest that being surveilled at home not only diminishes low-income Black mothers’ status in society, but also pushes them into deeper economic precarity. This research extends our understanding of the reach of surveillance into the lives of the poor even in spaces considered to be private.
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Turking in the time of COVID Arechar, Antonio A.; Rand, David G.
Behavior research methods,
12/2021, Volume:
53, Issue:
6
Journal Article
Peer reviewed
Open access
On March 16, 2020, the US Government introduced strict social distancing protocols for the United States in an effort to stem the spread of the COVID-19 pandemic. This had an immediate major effect ...on the job market, with millions of Americans forced to find alternative ways to make a living from home. As online labor markets like Amazon Mechanical Turk (MTurk) play a major role in social science research, concerns have been raised that the pandemic may be reducing the diversity of subjects participating in experiments. Here, we investigate this possibility empirically. Specifically, we look at 15,539 responses gathered in 23 studies run on MTurk between February and July 2020, examining the distribution of gender, age, ethnicity, political preference, and analytic cognitive style. We find notable changes on some of the measures following the imposition of nationwide social distancing: participants are more likely to be less reflective (as measured by the Cognitive Reflection Test), and somewhat less likely to be white, Democrats (traditionally over-represented on MTurk), and experienced with MTurk. Most of these differences are explained by an influx of new participants into the MTurk subject pool who are more diverse and representative – but also less attentive – than previous MTurkers.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
The term big data is currently a buzzword in social science, however its precise meaning is ambiguous. In this paper we focus on administrative data which is a distinctive form of big data. Exciting ...new opportunities for social science research will be afforded by new administrative data resources, but these are currently under appreciated by the research community. The central aim of this paper is to discuss the challenges associated with administrative data. We emphasise that it is critical for researchers to carefully consider how administrative data has been produced. We conclude that administrative datasets have the potential to contribute to the development of high-quality and impactful social science research, and should not be overlooked in the emerging field of big data.
•The meaning of big data is ambiguous.•There are many types of big data which offer distinct research opportunities.•Administrative social science data are particularly valuable.•The use of administrative data will place increased burden on the researcher.•The quality and accuracy of administrative data needs to be carefully evaluated.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
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