As the burden of misconduct in medical research is increasingly recognised, questions have been raised about how best to address this problem. Whilst there are existing mechanisms for the investigation and management of misconduct in medical literature, they are inadequate to deal with the magnitude of the problem. Journal editors and publishers play an essential role in protecting the veracity of the medical literature. Whilst ethical guidance for journal editors and publishers is important, it is not as readily enforceable as (...) legal obligations might be. This article questions the legal obligations that might exist for journal editors and publishing companies with respect to ensuring the veracity of the published literature. Ultimately, there is no enforceable legal obligation in Australia, the United Kingdom, or the United States. In light of this, more robust mechanisms are needed to deliver greater confidence and transparency in the investigative process, the management of concerns or findings of misconduct and the need to cleanse the literature. We show that the law disincentivises journals and publishers from ensuring truth in their publications. There are harmful consequences for medical care and public confidence in the medical profession and health care system when the foundations of medical science are questionable. (shrink)

Challenging moral relativism, Mitchell equips Christians to offer biblically faithful, theologically nuanced, and historically informed answers to the most pressing moral questions facing our world today.

A biblically informed guidebook for Christians facing difficult health care decisions, from the making of life (infertility, organ donation, cloning) and taking of life (abortion, euthanasia) to the technologically driven faking of life (genetic engineering, etc.).

The philosophical theory of scientific explanation proposed here involves a radically new treatment of causality that accords with the pervasively statistical character of contemporary science. Wesley C. Salmon describes three fundamental conceptions of scientific explanation--the epistemic, modal, and ontic. He argues that the prevailing view is untenable and that the modal conception is scientifically out-dated. Significantly revising aspects of his earlier work, he defends a causal/mechanical theory that is a version of the ontic conception. Professor Salmon's theory furnishes (...) a robust argument for scientific realism akin to the argument that convinced twentieth-century physical scientists of the existence of atoms and molecules. To do justice to such notions as irreducibly statistical laws and statistical explanation, he offers a novel account of physical randomness. The transition from the "reviewed view" of scientific explanation to the causal/mechanical model requires fundamental rethinking of basic explanatory concepts. (shrink)

Not since Ernest Nagel’s 1939 monograph on the theory of probability has there been a comprehensive elementary survey of the philosophical problems of probablity and induction. This is an authoritative and up-to-date treatment of the subject, and yet it is relatively brief and nontechnical. Hume’s skeptical arguments regarding the justification of induction are taken as a point of departure, and a variety of traditional and contemporary ways of dealing with this problem are considered. The author then sets forth his own (...) criteria of adequacy for interpretations of probability. Utilizing these criteria he analyzes contemporary theories of probability, as well as the older classical and subjective interpretations. (shrink)

"A rich collection. Since it holds a number of introductory pieces along with advanced essays and review articles, the volume will be accessible to a broad audience and will work well in philosophy of science courses....Essential."--Lawrence Sklar, University of Michigan.

Through his S–R model of statistical relevance, Wesley Salmon offers a solution to the scientific explanation of objectively improbable events.

This volume honors and examines the founders of the philosophy of logical empiricism. Historical and interpretive essays clarify the scientific philosophies of Carnap, Reichenbach, Hempel, Kant, and others, while exploring the main topics of logical empiricist philosophy of science.

Coauthored with Merrilee Salmon, addresses archaeologists and other anthropologists interested in the nature of scientific explanation. A group called the new archaeologists, concerned to assure the scientific status of archaeology, had become convinced that a sine qua non of science is the construction of explanations conforming to Hempel's D‐N model. The authors aim was to show that a much wider class of covering law models of explanation is available, and that others in this set are more suitable than the (...) D‐N model for archaeology and anthropology. At the same time, they show that the so‐called systems approach, advocated by other archaeologists, has all of the shortcomings of the D‐N model without offering improvements in exchange. (shrink)

This paper presents a drastically revised version of the theory of causality, based on analyses of causal processes and causal interactions, advocated in Salmon (1984). Relying heavily on modified versions of proposals by P. Dowe, this article answers penetrating objections by Dowe and P. Kitcher to the earlier theory. It shows how the new theory circumvents a host of difficulties that have been raised in the literature. The result is, I hope, a more satisfactory analysis of physical causality.

This work presents a stock/effort model describing both harvested fish population and fishing effort dynamics. The fishing effort dynamic is controlled by investment which corresponds to the revenue proportion generated by the activity. The dynamics are subject to a set of economic and biological state constraints. The analytical study focuses on the compatibility between state constraints and controlled dynamics. By using the mathematical concept of viability kernel, we reveal situations and management options that guarantee a sustainable system.

Outlines many different types of human understanding, and shows how scientific explanations enable us to understand the universe in which we live. It reflects on the great increases in scientific understanding during the past century, and exhibits the value of such understanding as we move into the twenty‐first century. The author recognizes the serious issues concerning values that face humanity at this time, and does not believe that science alone can solve these problems. He argues nevertheless that increased scientific understanding (...) can furnish the information we all need to formulate a responsible social policy. (shrink)

This chapter extends the discussion of the preceding chapter, and emphasizes the causal dimensions of explanation in archaeology. The author considers the sorts of situations that archaeologists want to explain, and notes that many of these are events that result from a complex set of factors, some of which are positively relevant to the occurrence of the event and others that are negatively relevant. In addition, many events that archaeologists want to explain are events that had a very low probability (...) of occurrence. The author argues that formal philosophical models of explanation, such as the D‐N and the I‐S models, are ill suited to the task of archaeological explanation. What is needed, he says, is the development of a probabilistic account of causality that can supplement notions of statistical relevance in explanations of archaeological phenomena. (shrink)

Examines three basic approaches to scientific explanation that have been advocated by influential writers in the second half of the twentieth century and are still held today. It shows how fundamental differences in these approaches emerge when they confront explanation in scientific contexts in which statistical laws and functional explanations play major roles. The author argues that the ontic conception, in which events are explained by showing how they fit into the physical patterns found in the world, is best equipped (...) to handle explanations that are problematic for the other approaches. (shrink)

Is a response to a criticism, posed by an anthropologist, of the Statistical Relevance model of explanation. The author claims that the objection does not hold. Although he believes that the model must be supplemented by causal considerations, he maintains that the S‐R schema constitutes a correct basis on which acceptable explanations can be erected. In answering Cartmill's question, the author examines the high‐probability requirement of Hempel's Inductive Statistical model, and clarifies some fundamental features of the S‐R model. His response (...) connects directly to van Fraassen's pragmatic theory, which is discussed from a different angle in Ch. 11. (shrink)

The fundamental tenet of logical empiricism is that the warrant for all scientific knowledge rests upon empirical evidence in conjunction with logic, where logic is taken to include induction or confirmation, as well as mathematics and formal logic (see evidence and confirmation). This appears to conflict strongly with Thomas Kuhn's famous statement that scientific theory choice depends on considerations that go beyond observation and logic, even when logic is construed so as to include confirmation (see kuhn and pragmatic factors in (...) theory acceptance). Logical empiricists deny the possibility of synthetic a priori knowledge ‐ that is, substantive knowledge of the world based on pure reason. Those who, with W. V. Quine, reject the analytic/synthetic distinction, would, I suppose, question the possibility of a priori knowledge altogether (see quine). Contemporary logical empiricists disagree, however, about such basic issues as the nature of empirical evidence, the status and structure of confirmation or inductive inference, the nature of scientific explanation, and the character of scientific theories, to name but a few examples (see theories; explanation; confirmation, paradoxes of). (shrink)

Evidence presented in Salmon (2001; Econometrica 69(6) 1597) indicates that typical tests to identify learning behavior in experiments involving normal form games possess little power to reject incorrect models. This paper begins by presenting results from an experiment designed to gather alternative data to overcome this problem. The results from these experiments indicate support for a learning-to-learn or rule learning hypothesis in which subjects change their decision rule over time. These results are then used to construct an adaptive learning (...) model which is intended to mimic more accurately the behavior observed. The final section of the paper presents results from a simple simulation based analysis comparing the performance of this adaptive learning model with that of several standard decision rules in reproducing the choice patterns observed in the experiment. (shrink)