Disparate Statistics

Statistical evidence is crucial throughout disparate impact’s three-stage analysis: during (1) the plaintiff’s prima facie demonstration of a policy’s disparate impact; (2) the defendant’s job-related business necessity defense of the discriminatory policy; and (3) the plaintiff’s demonstration of an alternative policy without the same discriminatory impact. The circuit courts are split on a vital question about the “practical significance” of statistics at Stage 1: Are “small” impacts legally insignificant? For example, is an employment policy that causes a one percent disparate impact an appropriate policy for redress through disparate impact litigation? This circuit split calls for a comprehensive analysis of practical significance testing across disparate impact’s stages. Importantly, courts and commentators use “practical significance” ambiguously between two aspects of practical significance: the magnitude of an effect and confidence in statistical evidence. For example, at Stage 1 courts might ask whether statistical evidence supports a disparate impact (a confidence inquiry) and whether such an impact is large enough to be legally relevant (a magnitude inquiry). Disparate impact’s texts, purposes, and controlling interpretations are consistent with confidence inquires at all three stages, but not magnitude inquiries. Specifically, magnitude inquiries are inappropriate at Stages 1 and 3—there is no discriminatory impact or reduction too small or subtle for the purposes of the disparate impact analysis. Magnitude inquiries are appropriate at Stage 2, when an employer defends a discriminatory policy on the basis of its job-related business necessity

How People Judge What Is Reasonable

A classic debate concerns whether reasonableness should be understood statistically (e.g., reasonableness is what is common) or prescriptively (e.g., reasonableness is what is good). This Article elaborates and defends a third possibility. Reasonableness is a partly statistical and partly prescriptive “hybrid,” reflecting both statistical and prescriptive considerations. Experiments reveal that people apply reasonableness as a hybrid concept, and the Article argues that a hybrid account offers the best general theory of reasonableness. First, the Article investigates how ordinary people judge what is reasonable. Reasonableness sits at the core of countless legal standards, yet little work has investigated how ordinary people (i.e., potential jurors) actually make reasonableness judgments. Experiments reveal that judgments of reasonableness are systematically intermediate between judgments of the relevant average and ideal across numerous legal domains. For example, participants’ mean judgment of the legally reasonable number of weeks’ delay before a criminal trial (ten weeks) falls between the judged average (seventeen weeks) and ideal (seven weeks). So too for the reasonable num- ber of days to accept a contract offer, the reasonable rate of attorneys’ fees, the reasonable loan interest rate, and the reasonable annual number of loud events on a football field in a residential neighborhood. Judgment of reasonableness is better predicted by both statistical and prescriptive factors than by either factor alone. This Article uses this experimental discovery to develop a normative view of reasonableness. It elaborates an account of reasonableness as a hybrid standard, arguing that this view offers the best general theory of reasonableness, one that applies correctly across multiple legal domains. Moreover, this hybrid feature is the historical essence of legal reasonableness: the original use of the “reasonable person” and the “man on the Clapham omnibus” aimed to reflect both statistical and prescriptive considerations. Empirically, reasonableness is a hybrid judgment. And normatively, reasonableness should be applied as a hybrid standard

A Defense of Scalar Utilitarianism

Scalar Utilitarianism eschews foundational notions of rightness and wrongness in favor of evaluative comparisons of outcomes. I defend Scalar Utilitarianism from two critiques, the first against an argument for the thesis that Utilitarianism's commitments are fundamentally evaluative, and the second that Scalar Utilitarianism does not issue demands or sufficiently guide action. These defenses suggest a variety of more plausible Scalar Utilitarian interpretations, and I argue for a version that best represents a moral theory founded on evaluative notions, and offers better answers to demandingness concerns than does the ordinary Scalar Utilitarian response. If Utilitarians seek reasonable development and explanation of their basic commitments, they may wish to reconsider Scalar Utilitarianism

Does religious belief impact philosophical analysis?

One popular conception of natural theology holds that certain purely rational arguments are insulated from empirical inquiry and independently establish conclusions that provide evidence, justification, or proof of God’s existence. Yet, some raise suspicions that philosophers and theologians’ personal religious beliefs inappropriately affect these kinds of arguments. I present an experimental test of whether philosophers and theologians’ argument analysis is influenced by religious commitments. The empirical findings suggest religious belief affects philosophical analysis and offer a challenge to theists and atheists, alike: reevaluate the scope of natural theology’s conclusions or acknowledge and begin to address the influence of religious belief

Geodata

Empirical data can be characterized by a precise location in space and time. An estimated 80% of all data holds such a spatio-temporal reference and is termed geodata. This paper starts with the question: What is the additional benefit for socio-economic sciences using geodata and the spatial dimension respectively? In the following a multidimensional approach is chosen to outline the Status Quo of geodata and spatial techniques in Germany. It is particularly the continuously growing amount and the variety of available geodata which is stated. Data security is an issue of high importance when using geodata. Furthermore, the present developments in price and user concepts, accessibility, technical standards and institutionalisation are addressed. A number of challenges concerning the field of geodata are identified including the open access to geodata, data security issues and standardization. The main challenge however seems to be the exchange between the rather segregated fields of geoinformation and the information infrastructure. Furthermore, the census 2011 is identified as a major challenge for the acquisition and management of geodata. Geodata and spatial techniques are a rapidly developing field due to technology developments of data and methods as well as due to recently growing public interest. Their additional be efit for socioeconomic research should be exploited in the future.geodata, geoinformation, Web-GIS, geodata-infrastructure, spatial techniques

Geographically Referenced Data for Social Science

An estimated 80% of all information has a spatial reference. Information about households as well as environmental data can be linked to precise locations in the real world. This offers benefits for combining different datasets via the spatial location and, furthermore, spatial indicators such as distance and accessibility can be included in analyses and models. HSpatial patterns of real-world social phenomena can be identified and described and possible interrelationships between datasets can be studied. Michael F. GOODCHILD, a Professor of Geography at the University of California, Santa Barbara and principal investigator at the Center for Spatially Integrated Social Science (CSISS), summarizes the growing significance of space, spatiality, location, and place in social science research as follows: "(...) for many social scientists, location is just another attribute in a table and not a very important one at that. After all, the processes that lead to social deprivation, crime, or family dysfunction are more or less the same everywhere, and, in the minds of social scientists, many other variables, such as education, unemployment, or age, are far more interesting as explanatory factors of social phenomena than geographic location. Geographers have been almost alone among social scientists in their concern for space; to economists, sociologists, political scientists, demographers, and anthropologists, space has been a minor issue and one that these disciplines have often been happy to leave to geographers. But that situation is changing, and many social scientists have begun to talk about a "spatial turn," a new interest in location, and a new "spatial social science" that crosses the traditional boundaries between disciplines. Interest is rising in GIS (Geographic Information Systems) and in what GIS makes possible: mapping, spatial analysis, and spatial modelling. At the same time, new tools are becoming available that give GIS users access to some of the big ideas of social science."

Folk teleology drives persistence judgments

Two separate research programs have revealed two different factors that feature in our judgments of whether some entity persists. One program—inspired by Knobe—has found that normative considerations affect persistence judgments. For instance, people are more inclined to view a thing as persisting when the changes it undergoes lead to improvements. The other program—inspired by Kelemen—has found that teleological considerations affect persistence judgments. For instance, people are more inclined to view a thing as persisting when it preserves its purpose. Our goal in this paper is to determine what causes persistence judgments. Across four studies, we pit normative considerations against teleological considerations. And using causal modeling procedures, we find a consistent, robust pattern with teleological and not normative considerations directly causing persistence judgments. Our findings put teleology in the driver’s seat, while at the same time shedding further light on our folk notion of an object

Managing Research Data in Big Science

The project which led to this report was funded by JISC in 2010--2011 as part of its 'Managing Research Data' programme, to examine the way in which Big Science data is managed, and produce any recommendations which may be appropriate. Big science data is different: it comes in large volumes, and it is shared and exploited in ways which may differ from other disciplines. This project has explored these differences using as a case-study Gravitational Wave data generated by the LSC, and has produced recommendations intended to be useful variously to JISC, the funding council (STFC) and the LSC community. In Sect. 1 we define what we mean by 'big science', describe the overall data culture there, laying stress on how it necessarily or contingently differs from other disciplines. In Sect. 2 we discuss the benefits of a formal data-preservation strategy, and the cases for open data and for well-preserved data that follow from that. This leads to our recommendations that, in essence, funders should adopt rather light-touch prescriptions regarding data preservation planning: normal data management practice, in the areas under study, corresponds to notably good practice in most other areas, so that the only change we suggest is to make this planning more formal, which makes it more easily auditable, and more amenable to constructive criticism. In Sect. 3 we briefly discuss the LIGO data management plan, and pull together whatever information is available on the estimation of digital preservation costs. The report is informed, throughout, by the OAIS reference model for an open archive