This study brings together a number of related critical issues, including the causal laws that attempt to govern fictional worlds, the reader's implication in the causal dilemmas that confront major characters, and the philosophical and ideological ascriptions of cause that are variously embodied, interrogated, or parodied. One of the most significant features of this study is its disclosure of just how fundamental and widespread causal issues are in complex narratives - and how insistently they are thematized in twentieth-century works.

The tendency among working journalists to view ethics negatively, and ethical decision making as an attempt to inhibit their work by post facto sniping, is in part attributable to the way ethics is still taught in some undergraduate journalism programs - as a compendium of what journalists should not do. By adopting an approach that helps students and working journalists recognize ethics as affirmative, systematic, integrative, and definitive, ethics teachers can help foster a conception of ethics as positive, helpful, and (...) fundamental to the duty to inform. (shrink)

As the basis for federal and state freedom of information laws, the legal idea of a public right to know has been a blessing. As the often-invoked moral justification for the press's right to publish, however, it is dangerous, because an unfettered right to know would result in restrictions on the press's right to determine what to publish. By acknowledging their moral responsibility to provide audiences with information based on their need to know, journalists can avoid the hazards of arguing (...) that an unrestricted public right to know is the foundation of press freedom. (shrink)

Machine Learning has become a popular tool in a variety of applications in criminal justice, including sentencing and policing. Media has brought attention to the possibility of predictive policing systems causing disparate impacts and exacerbating social injustices. However, there is little academic research on the importance of fairness in machine learning applications in policing. Although prior research has shown that machine learning models can handle some tasks efficiently, they are susceptible to replicating systemic bias of previous human decision-makers. While there (...) is much research on fair machine learning in general, there is a need to investigate fair machine learning techniques as they pertain to the predictive policing. Therefore, we evaluate the existing publications in the field of fairness in machine learning and predictive policing to arrive at a set of standards for fair predictive policing. We also review the evaluations of ML applications in the area of criminal justice and potential techniques to improve these technologies going forward. We urge that the growing literature on fairness in ML be brought into conversation with the legal and social science concerns being raised about predictive policing. Lastly, in any area, including predictive policing, the pros and cons of the technology need to be evaluated holistically to determine whether and how the technology should be used in policing. (shrink)

Over several years, genetic studies in the model system, Drosophila melanogastor, have uncovered genes that when mutated, lead to a block in myoblast fusion. Analyses of these gene products have suggested that Arp2/3‐mediated regulation of the actin cytoskeleton is crucial to myoblast fusion in the fly. Recent advances in imaging in Drosophila embryos, both in fixed and live preparations, have led to a new appreciation of both the three‐dimensional organization of the somatic mesoderm and the cell biology underlying myoblast fusion. (...) BioEssays 30:423–431, 2008. © 2008 Wiley Periodicals, Inc. (shrink)

An amendment to this paper has been published and can be accessed via a link at the top of the paper.