Constructing Trustworthy Historical Narratives: Criteria,Principles and Techniques

Organizational scholars increasingly recognize the value of employing historical research. Yet the fields of history and organization studies struggle to reconcile. In this paper, the authors contend that a closer connection between these two fields is possible if organizational historians bring their role in the construction of historical narratives to the fore and open up their research decisions for discussion. They provide guidelines to support this endeavor, drawing on four criteria that are prevalent within interpretive organization studies for developing the trustworthiness of research: credibility; confirmability; dependability; and transferability. In contrast to the traditional use of trustworthiness criteria to evaluate the quality of research, the authors advance the criteria to encourage historians to generate more transparent narratives. Such transparency allows others to comprehend and comment on the construction of narratives, thereby building trust and understanding. Each criterion is converted into a set of guiding principles to enhance the trustworthiness of historical research, pairing each principle with a practical technique gleaned from a range of disciplines within the social sciences to provide practical guidance.     

A Probabilistic Framework for Risk Analysis of Widespread Flood Events: A Proof‐of‐Concept Study

This article presents a flood risk analysis model that considers the spatially heterogeneous nature of flood events. The basic concept of this approach is to generate a large sample of flood events that can be regarded as temporal extrapolation of flood events. These are combined with cumulative flood impact indicators, such as building damages, to finally derive time series of damages for risk estimation. Therefore, a multivariate modeling procedure that is able to take into account the spatial characteristics of flooding, the regionalization method top‐kriging, and three different impact indicators are combined in a model chain. Eventually, the expected annual flood impact (e.g., expected annual damages) and the flood impact associated with a low probability of occurrence are determined for a study area. The risk model has the potential to augment the understanding of flood risk in a region and thereby contribute to enhanced risk management of, for example, risk analysts and policymakers or insurance companies. The modeling framework was successfully applied in a proof‐of‐concept exercise in Vorarlberg (Austria). The results of the case study show that risk analysis has to be based on spatially heterogeneous flood events in order to estimate flood risk adequately.     

Inference and Decision Making for 21st-Century Drug Development and Approval

ABSTRACT

The cost and time of pharmaceutical drug development continue to grow at rates that many say are unsustainable. These trends have enormous impact on what treatments get to patients, when they get them and how they are used. The statistical framework for supporting decisions in regulated clinical development of new medicines has followed a traditional path of frequentist methodology. Trials using hypothesis tests of “no treatment effect” are done routinely, and the p-value < 0.05 is often the determinant of what constitutes a “successful” trial. Many drugs fail in clinical development, adding to the cost of new medicines, and some evidence points blame at the deficiencies of the frequentist paradigm. An unknown number effective medicines may have been abandoned because trials were declared “unsuccessful” due to a p-value exceeding 0.05. Recently, the Bayesian paradigm has shown utility in the clinical drug development process for its probability-based inference. We argue for a Bayesian approach that employs data from other trials as a “prior” for Phase 3 trials so that synthesized evidence across trials can be utilized to compute probability statements that are valuable for understanding the magnitude of treatment effect. Such a Bayesian paradigm provides a promising framework for improving statistical inference and regulatory decision making.     

Embracing Indeterminacy: On Being a Liminal Professional

The rise of the corporate profession has contributed to a more varied and ambiguous professional terrain that is increasingly seen to be indeterminate and fluid. This paper advances the current debate around the development of corporate professions, exploring how practitioners respond to this environment. Drawing on research with public relations practitioners, the paper shows how the idea of being a liminar facilitates the formation of a professional identity in conditions of high indeterminacy. In taking an individual level of analysis of professions, the paper suggests that indeterminacy is a more resonant feature for corporate professionals than previously suggested in the research, but that this indeterminacy is navigated in professional identity construction through ‘being a liminar’, and thus greater nuance may need to be recognized in the conceptualization of both corporate professions and corporate professionalization. It also demonstrates the use of liminality as a discursive resource in identity construction and with it, challenges the common association of liminality with self‐doubt and existential anxiety. In turn, the paper considers the implications of the liminal professional identity for the future of contemporary professions, and for understanding the liminal experience.     

A pixel level evaluation of five multitemporal global gridded population datasets: a case study in Sweden, 1990–2015

Population and Environment - Human activity is a major driver of change and has contributed to many of the challenges we face today. Detailed information about human population distribution is...     

Ethical Numbers: Ethics Training in U.S. Graduate Statistics Programs, 2013–2014

As important members of research teams, statisticians bear an ethical responsibility to analyze, interpret, and report data honestly and objectively. One way of reinforcing ethical responsibilities is through required courses covering a variety of ethics-related topics at the graduate level. We assessed ethics requirements for graduate-level statistics training programs in the United States for the 2013–2014 academic year using the websites of 88 universities, examining 103 biostatistics programs, and 136 statistics degree programs. We categorized programs’ ethics training requirements as required or not required. Thirty-one (35.1%) universities required an ethics course for at least some degree students. Sixty-two (25.5%) degree programs required an ethics course for at least some students. The majority (77.4%) of required courses were worth 0 or 1 credit. Of the 177 programs without an ethics requirement, 19 (10.7%) listed an ethics elective. Although a single ethics course is insufficient for instilling an ethical approach to science, degree programs that model expectations through coursework point to the value of ethics in science. More training programs should prepare statisticians to consider the ethical dimensions of their work through required coursework. Supplementary materials for this article are available online.     

Foreign-born and native-born migration in the U.S.: evidence from linked IRS administrative and census survey records

Journal of Population Research - This paper details efforts to link administrative records from the Internal Revenue Service (IRS) to American Community Survey (ACS) and 2010 Census microdata for...     

Sex Related Factors in the Perception of Threatening Facial Expressions

This study replicated and extended previously reported sex differences involving both viewer and target in the recognition of threatening facial expressions. Based on the assumption that the evolved cognitive mechanisms mediating anger recognition would have been designed by natural selection to operate quickly in the interests of survival, brief tachistoscopic presentation of stimulus photographs was used. Additionally, in contrast to prior published studies, the statistical methods of signal detection research were used to control for the confounding effects of non-random guessing. The main hypothesis, that anger posed by males would be more accurately perceived than anger posed by females, was supported. A secondary hypothesis, that female-posed anger would be more accurately perceived by women than by men, received partial support. Testosterone levels, measured inferentially in terms of diurnal cycles, failed to show the hypothesized positive relationship to accuracy of anger perception.