Biomedicine: an ontological dissection

Though ubiquitous across the medical social sciences literature, the term “biomedicine” as an analytical concept remains remarkably slippery. It is argued here that this imprecision is due in part to the fact that biomedicine is comprised of three interrelated ontological spheres, each of which frames biomedicine as a distinct subject of investigation. This suggests that, depending upon one’s ontological commitment, the meaning of biomedicine will shift. From an empirical perspective, biomedicine takes on the appearance of a scientific enterprise and is defined as a derivative category of Western science more generally. From an interpretive perspective, biomedicine represents a symbolic-cultural expression whose adherence to the principles of scientific objectivity conceals an ideological agenda. From a conceptual perspective, biomedicine represents an expression of social power that reflects structures of power and privilege within capitalist society. No one perspective exists in isolation and so the image of biomedicine from any one presents an incomplete understanding. It is the mutually-conditioning interrelations between these ontological spheres that account for biomedicine’s ongoing development. Thus, the ontological dissection of biomedicine that follows, with particular emphasis on the period of its formal crystallization in the latter nineteenth and early twentieth century, is intended to deepen our understanding of biomedicine as an analytical concept across the medical social sciences literature.

A proposed universal model of problem solving for design,science and cognate fields

A modestly generic, innovative, problem solving process with roots in the study of design and scientific research problem solving is presented and motivated. It is argued to be the shared core process of all problem solving. At its heart is a recognition of five foci or nodes of change vital to the process (changes in problem and solution formulation, method, constraints, and partial solution proposals) together with a bootstrap marked by the formation of higher order knowledge about problem solving in the domain in tandem with the solving of specific problems, the essential feature of all learned improvement. None of these elements is entirely original, but the way they are made explicit and developed (rather than folded into fewer, more abstract, boxes) is argued to provide fresh understanding of the organisation and power of the process to deal with complex practical problems.     

Paul Feyerabend und Thomas Kuhn

The paper discusses some aspects of the relationship between Feyerabend and Kuhn. First, some biographical remarks concerning their connections are made. Second, four characteristics of Feyerabend and Kuhn's concept of incommensurability are discussed. Third, Feyerabend's general criticism of Kuhn's Structure of Scientific Revolutions is reconstructed. Forth and more specifically, Feyerabend's criticism of Kuhn's evaluation of normal science is critically investigated. Finally, Feyerabend's re-evaluation of Kuhn's philosophy towards the end of his life is presented. This revised version was published online in August 2006 with corrections to the Cover Date.     

Necessity (not just novelty) is the mother of invention: using creativity research to improve research in work and organizational psychology

In this essay, we discuss reasons that work and organizational psychology does not live up to its self-declared mission of being an applied science in the service of improving both people’s quality of working life and organizational effectiveness. We use fundamentals of research on creativity and innovation as a lens through which we can view problems and possible solutions to these problems. In particular, we stress that innovation entails not only new, but also useful insights, that innovation requires “rewarding failure”, and that innovation feeds off of team diversity. We provide suggestions for how the definition of theoretical and empirical contributions of research, reward systems, and collaboration practices could be changed to foster innovative research that helps people thrive at work.     

The importance of understanding: Model space moderates goal specificity effects

The three-space theory of problem solving predicts that the quality of a learner's model and the goal specificity of a task interact on knowledge acquisition. In Experiment 1 participants used a computer simulation of a lever system to learn about torques. They either had to test hypotheses (nonspecific goal), or to produce given values for variables (specific goal). In the good- but not in the poor-model condition they saw torque depicted as an area. Results revealed the predicted interaction. A nonspecific goal only resulted in better learning when a good model of torques was provided. In Experiment 2 participants learned to manipulate the inputs of a system to control its outputs. A nonspecific goal to explore the system helped performance when compared to a specific goal to reach certain values when participants were given a good model, but not when given a poor model that suggested the wrong hypothesis space. Our findings support the three-space theory. They emphasize the importance of understanding for problem solving and stress the need to study underlying processes.     

A selectionist explanation for the success and failures of science

I argue that van Fraassen’s selectionist explanation for the success of science is superior to the realists’ explanation. Whereas realists argue that our current theories are successful because they accurately reflect the structure of the world, the selectionist claims that our current theories are successful because unsuccessful theories have been eliminated. I argue that, unlike the explanation proposed by the realist, the selectionist explanation can also account for the failures of once successful theories and the fact that sometimes two competing theories are both equally successful.

A Pragmatic Case against Pragmatic Scientific Realism

Pragmatic Scientific Realism (PSR) urges us to take up the realist aim or the goal of truth although we have good reason to think that the goal can neither be attained nor approximated. While Newton-Smith thinks that pursuing what we know we cannot achieve is clearly irrational, Rescher disagrees and contends that pursuing an unreachable goal can be rational on pragmatic grounds—if in pursuing the unreachable goal one can get indirect benefits. I have blocked this attempt at providing a pragmatic justification for the realist aim of PSR on precisely the same pragmatic grounds—since there is a competing alternative to PSR, and the alternative can provide whatever indirect benefits PSR can offer while being less risky than it is, prudential reasoning favours the alternative to PSR. This undermines the pragmatic case for the realist aim of science since the instrumentalist alternative does not aim at the truth.     

Emerging Infectious Diseases: Coping with Uncertainty

The world’s scientific community must be in a state of constant readiness to address the threat posed by newly emerging infectious diseases. Whether the disease in question is SARS in humans or BSE in animals, scientists must be able to put into action various disease containment measures when everything from the causative pathogen to route(s) of transmission is essentially uncertain. A robust epistemic framework, which will inform decision-making, is required under such conditions of uncertainty. I will argue that this framework should have reasoning at its center and, specifically, that forms of reasoning beyond deduction and induction should be countenanced by scientists who are confronted with emerging infectious diseases. In previous articles, I have presented a case for treating certain so-called traditional informal fallacies as rationally acceptable forms of argument that can facilitate scientific inquiry when little is known about an emerging disease. In this article, I want to extend that analysis by highlighting the unique features of these arguments that makes them specially adapted to cope with conditions of uncertainty. Of course, such a view of the informal fallacies must at least be consistent with the reasoning practices of scientists, and particularly those scientists (viz. epidemiologists) whose task it is to track and respond to newly emerging infectious diseases. To this end, I draw upon examples of scientific reasoning from the UK’s BSE crisis, a crisis that posed a significant threat to both human and animal health.