This paper identifies two conceptions of security in contemporary concerns over the vulnerability of computers and networks to hostile attack. One is derived from individual-focused conceptions of computer security developed in computer science and engineering. The other is informed by the concerns of national security agencies of government as well as those of corporate intellectual property owners. A comparative evaluation of these two conceptions utilizes the theoretical construct of “securitization,”developed by the Copenhagen School of International Relations.

In times of global economic and political crises, the notion of solidarity is gaining new currency. This book argues that a solidarity-based perspective can help us to find new ways to address pressing problems. Exemplified by three case studies from the field of biomedicine: databases for health and disease research, personalised healthcare, and organ donation, it explores how solidarity can make a difference in how we frame problems, and in the policy solutions that we can offer.

A considerable number of areas of bioscience, including gene and drug discovery, metabolic engineering for the biotechnological improvement of organisms, and the processes of natural and directed evolution, are best viewed in terms of a ‘landscape’ representing a large search space of possible solutions or experiments populated by a considerably smaller number of actual solutions that then emerge. This is what makes these problems ‘hard’, but as such these are to be seen as combinatorial optimisation problems that are best attacked (...) by heuristic methods known from that field. Such landscapes, which may also represent or include multiple objectives, are effectively modelled in silico, with modern active learning algorithms such as those based on Darwinian evolution providing guidance, using existing knowledge, as to what is the ‘best’ experiment to do next. An awareness, and the application, of these methods can thereby enhance the scientific discovery process considerably. This analysis fits comfortably with an emerging epistemology that sees scientific reasoning, the search for solutions, and scientific discovery as Bayesian processes. (shrink)

In this long-awaited compendium of new and newly revised essays, Alison Wylie explores how archaeologists know what they know. -/- Preprints available for download. Please see entry for specific article of interest.

*PSA 2016, symposium on “Data in Time: Epistemology of Historical Data” organized by Sabina Leonelli, 5 November 2016* *See published version: "Radiocarbon Dating in Archaeology: Triangulation and Traceability" in Data Journeys in the Sciences (2020) - link below* Archaeologists put a premium on pressing “legacy data” into service, given the notoriously selective and destructive nature of their practices of data capture. Legacy data consist of material and records that been assembled over decades, sometimes centuries, often by means and for purposes (...) long since discredited or superseded. The primary strategies by which archaeologists put the data to work for new purposes are, I argue, secondary retrieval, recontextualization, and experimental modelling. I focus here on a particularly telling and complex example of secondary retrieval: the extraction of new data from old by means of radiocarbon dating. This is by no means a straightforward process of retrieving physical samples from legacy data to which 14C techniques can be applied that can, on their own, decisively settle chronological questions. When Libby’s post-war radiocarbon revolution got under way, it was expected to establish an absolute chronology that would render obsolete the local and relative chronologies on which archaeologists had long relied. Transformative though it has been, bringing these tools of physical dating to bear on archaeological problems has been a long, tortuous process, now described as proceeding through two subsequent radiocarbon revolutions. The second was an extended process of calibration by which 14C chronologies were corrected and refined, often against the very lines of evidence they were meant to displace. The most recent, a “pragmatic Bayesian” approach to archaeological dating, is motivated by concern that, no matter how much it is refined, radiocarbon dating cannot on its own resolve the chronological problems that archaeologists address; the challenge, its advocates argue, is to ‘fully integrate archaeological information with 14C dating in order to address archaeologically relevant timescales and episodes’. This is a genre of “robustness” reasoning that illustrates its epistemic risks as well as its appeal. As recent philosophical debate makes clear, it depends on appeals to the convergence of independent lines of evidence that may have more rhetorical than epistemic force and that may be spurious. Drawing on this philosophical literature I identify a set of conditions that must be met if these risks are to be avoided, all of which are an explicit focus of debate in cases of contestation about and reconciliation of chronologies based on legacy data. (shrink)

The biosciences have become information sciences, in which knowledge is often produced in silica, by the manipulation and analysis of large datasets. Genomics has been at the forefront of the data explosion and is a model for bioscience as a large-scale endeavor. Large genome research datasets are frequently shared through research repositories. To protect the interests of people from whom the data were derived, human data are often shared through a controlled access mechanism, in which data repositories can, in theory, (...) place limitations on who uses the data and for what purpose. Controlled access is an innovative governance mechanism, but it may not protect data sources the way policy makers intended. Here, I describe one controlled access process in some detail, and provide insight into how and why researchers fail to comply with data use restrictions. (shrink)

The biosciences have become information sciences, in which knowledge is often produced in silica, by the manipulation and analysis of large datasets. Genomics has been at the forefront of the data explosion and is a model for bioscience as a large-scale endeavor. Large genome research datasets are frequently shared through research repositories. To protect the interests of people from whom the data were derived , human data are often shared through a controlled access mechanism, in which data repositories can, in (...) theory, place limitations on who uses the data and for what purpose. Controlled access is an innovative governance mechanism, but it may not protect data sources the way policy makers intended. Here, I describe one controlled access process in some detail, and provide insight into how and why researchers fail to comply with data use restrictions. (shrink)