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This article considers the practice of hacking in terms of making and modifying three-dimensional objects. In line with the work of a number of prominent thinkers in the field, the practice is considered to be a deliberate act by end users to not only understand, make accessible, de-alienate, appropriate and personalise products but also to demystify what Latour refers to as the 'black box' effect of established product archetypes. Where hacking is typically considered a post-production process, it is argued here that upfront, design-led approaches intended to harness downstream end-user post-production hacking (pre-hack) are in line with Jones' call from over three decades ago for design divergence and continuity through collaborative processes – which we now find in the field of Open Design. This is discussed in light of the broader context of sustainability, which needs innovation from the ground up as well as top down.

AbstractStudies of turnover in the federal public service often study intention rather than behavior and studies that evaluate the reliability of intention as a proxy for that behavior are rare. This article uses a novel data set that measures exit intention and behavior for the same senior civil servants to evaluate two aspects of the reliability of turnover intention as a proxy for behavior: (1) prediction error and (2) similarity of marginal effects. Overall, exit intention is a problematic proxy for behavior because it consistently overestimates the probability of exit. While coefficients from models of intent tend to have the correct sign, the magnitudes of marginal effects can be misleading, tending to overestimate corresponding marginal effects in models of exit behavior. Moreover, coefficients do sometimes change signs between models. The article discusses the implications of these findings and suggests the next steps to better use exit intention to understand exit behavior.

AbstractTo his supporters, one very attractive feature of Donald Trump's candidacy was his prior experience as a business executive. Trump promised that he would run America like a business and would select the very best people. In this article, we evaluate President Trump's approach to managing the executive branch by scrutinizing his approach to personnel. We reference two sources of important new data. We first examine data on nominations to all Senate‐confirmed positions during the last three presidencies. We also use data from the 2007, 2014, and 2020 (preliminary) Surveys on the Future of Government Service, surveys of thousands of appointed and career federal executives, to determine whether the president selected the very best people. We conclude by using the survey data to evaluate whether the president's approach led to effective management. A close look at the new data reveals that the president has been slow to nominate officials to key positions. Federal executives rate the persons the president has appointed as less competent than appointees from the Bush administration or civil servants from the current one. The survey data also reveal that the president's approach has done nothing to arrest the decline in the capacity of the public service. We conclude by discussing the implications of the Trump presidency for our traditional understanding of the president as chief executive. Specifically, we discuss whether we should reevaluate common beliefs about presidents caring inherently about the management of the executive.

AbstractGiven pervasive gridlock at the national level, state legislatures are increasingly the place where notable policy change occurs. Investigating such change is difficult because it is often hard to characterise policy change and use observable data to evaluate theoretical predictions; it is subsequently unclear whether law-making explanations focusing on the US Congress also apply to state legislatures. We use several measures of state policy outcomes to examine lawmaking in state legislatures across nearly two decades, and we argue for using simulation studies to connect theoretical predictions to empirical specifications and help interpret the theoretical relevance of estimated correlations. Doing so reveals that the observed law-making outcomes we study are most consistent with law-making models emphasising the importance of the chamber median and the powers of the governor rather than those that focus on the preferences of the majority party.

We present a real-time three-dimensional automatic target recognition approach appropriate for future light detection and ranging-based missiles. Our technique extends the speeded-up robust features method into the third dimension by solving multiple two-dimensional problems and performs template matching based on the extreme case of a single pose per target. Evaluation on military targets shows higher recognition rates under various transformations and perturbations at lower processing time compared to state-of-the-art approaches.

AbstractAcross disciplines and contexts, scholars vary in their conception of politicization. This variation reveals a need for more consistent concept definition and measurement of the extent to which politics influence administration. We engage this need by providing a typology of politicization that includes consideration of the actions of elected officials, political appointees, and merit‐based administrators. We then assess dynamic differences across the typology in the United States using personnel records spanning four decades, measures of insulation derived from statutory language, and three surveys of senior federal executives. Our analysis provides a research agenda for scholars who seek to examine the politicization of the executive branch.

We propose an architecture appropriate for future Light Detection and Ranging (LIDAR) active homing seeker missiles with Automatic Target Recognition (ATR) capabilities. Our proposal enhances military targeting performance by extending ATR into the 3rd dimension. From a military and aerospace industry point of view, this is appealing as weapon effectiveness against camouflage, concealment and deception techniques can be substantially improved. Specifically, we present a missile seeker 3D ATR architecture that relies on the 3D local feature based SHOT descriptor and a dual-role pipeline with a number of pre and post-processing operations. We evaluate our architecture on a number of missile engagement scenarios in various environmental setups with the missile being under various altitudes, obliquities, distances to the target and scene resolutions. Under these demanding conditions, the recognition performance gained is highly promising. Even in the extreme case of reducing the database entries to a single template per target, our interchangeable ATR architecture still provides a highly acceptable performance. Although we focus on future intelligent missile systems, our approach can be implemented to a great range of time-critical complex systems for space, air and ground environments for military, law-enforcement, commercial and research purposes.

We propose an architecture appropriate for future Light Detection and Ranging (LIDAR) active homing seeker missiles with Automatic Target Recognition (ATR) capabilities. Our proposal enhances military targeting performance by extending ATR into the 3rd dimension. From a military and aerospace industry point of view, this is appealing as weapon effectiveness against camouflage, concealment and deception techniques can be substantially improved. Specifically, we present a missile seeker 3D ATR architecture that relies on the 3D local feature based SHOT descriptor and a dual-role pipeline with a number of pre and post-processing operations. We evaluate our architecture on a number of missile engagement scenarios in various environmental setups with the missile being under various altitudes, obliquities, distances to the target and scene resolutions. Under these demanding conditions, the recognition performance gained is highly promising. Even in the extreme case of reducing the database entries to a single template per target, our interchangeable ATR architecture still provides a highly acceptable performance. Although we focus on future intelligent missile systems, our approach can be implemented to a great range of time-critical complex systems for space, air and ground environments for military, law-enforcement, commercial and research purposes.