In this article, I offer a partial analysis of the role of values in qualitative data collection in social research. The partial analysis shows that nonepistemic values have both required and permissible roles to play during this phase of research. By appeal to the analysis, I reject the ideal of value-free science as applied to qualitative data collection, and I demonstrate why two alternative ideals should likewise be dismissed as standards for values in qualitative data collection. Also, I briefly discuss the extent to which the partial analysis carries over to quantitative data collection in social research.

Philosophy of science is the organized expression of a growing intent among philosophers and scientists to clarify, perhaps unify, the programs, methods and results of the disciplines of philosophy and of science. The examination of fundamental concepts and presuppositions in the light of the positive results of science, systematic doubt of the positive results, and a thorough-going analysis and critique of logic and of language, are typical projects for this joint effort. It is not necessary to be committed to a belief that science and philosophy are or should be one, or else that “never the twain shall meet.” If anything, there is to be expected a wholesome regard for the value of established science in furnishing a foil for philosophy and a check on its old extravagances. This does not mean that even the best-established science may not be subject to most devastating criticism by analysis of its foundations. In fact, despairing of the philosophy of the schools, science has done it largely for itself. The theories of gravitation, atomicity, electro-magnetism, evolution, relativity and quanta have all arisen through drastic revisions of complacent fundamental “truths.”

The Gibbs’ Paradox is commonly explained by invoking some type of “principle of indistinguishability,” which asserts that the interchange of identical particles is not a real physical event, i.e., is operationally meaningless. However, if this principle is to provide a satisfactory resolution of the Paradox, it must be operationally possible to determine whether, in fact, two given systems are identical or not. That is, the assertion that the Gibbs’ Paradox is resolvable by an indistinguishability principle actually is an assertion that we can in principle possess a complete set of effective procedures for determining the identity or non-identity of arbitrary physical systems. We show that, in rather general situations, an assertion of this type is not well founded. It is further pointed out that a failure to recognize an incomplete set of “sameness criteria” can lead to serious blunders in physics and in biology.

The concept of disease remains hotly contested. In light of problems with existing accounts, some theorists argue that the disease concept ought to be eliminated. We answer this skeptical challenge by reframing the discussion in terms of the role that the disease concept plays in the complex network of health care institutions in which it is deployed. We argue that while prevailing accounts do not suffer from the particular defects that critics have identified, they do suffer from other deficits, and this leads us to propose a new account that satisfies the desiderata for a concept of disease in human medicine.

I discuss a broad critique of the classical approach to the foundations of statistical mechanics (SM) offered by N. S. Krylov. He claims that the classical approach is in principle incapable of providing the foundations for interpreting the “laws” of statistical physics. Most intriguing are his arguments against adopting a de facto attitude towards the problem of irreversibility. I argue that the best way to understand his critique is as setting the stage for a positive theory which treats SM as a theory in its own right, involving a completely different conception of a system's state. As the orthodox approach treats SM as an extension of the classical or quantum theories (one which deals with large systems), Krylov is advocating a major break with the traditional view of statistical physics.

The growing acceptance and success of experimental economics has increased the interest of researchers in tackling philosophical and methodological challenges to which their work increasingly gives rise. I sketch some general issues that call for the combined expertise of experimental economists and philosophers of science, of experiment, and of inductive-statistical inference and modeling.

This paper proposes a revision of our understanding of causation that is designed to address what Hartry Field has suggested is the central problem in the metaphysics of causation today: reconciling Bertrand Russell's arguments that the concept of causation can play no role in the advanced sciences with Nancy Cartwright's arguments that causal concepts are essential to a scientific understanding of the world. The paper shows that Russell's main argument is, ironically, very similar to an argument that Cartwright has put forward against the truth of universal laws of nature. The paper uses this insight to develop an account of causation that does justice to traditional views yet avoids the arguments of Russell.

The disagreement with regard to the validity of the principle of causality, existing to-day among scientists, has its roots in the diversity of definitions of the principle itself rather than in a problematic scientific situation. As far as the formulation of quantum theory is complete its bearing upon philosophical questions can be fixed with precision provided the questions are phrased intelligibly. But a question is intelligible from a scientific point of view only if it satisfies two conditions: (1) the meaning of its terms must be fixed; (2) it must be in accord with the conventions of the science to which the question is put.

Combining quantum mechanics with special relativity requires (i) that a spacetime representation of quantum states be found; (ii) that such states, represented as extended along equal-time hyperplanes, be invariant when transformed from one frame to another; and (iii) that collapses of states be instantaneous in every frame. These requirements are met using branching spacetime, in which probabilities of outcomes are represented by the numerical proportions of branches on which the outcomes occur. Quantum states of systems are then identified with the probability values, built into spacetime along spacelike hypersurfaces, of all possible outcomes of all possible tests to which the systems can be subjected.

Standard models of statistical explanation face two intractable difficulties. In his 1984 Salmon argues that because statistical explanations are essentially probabilistic we can make sense of statistical explanation only by rejecting the intuition that scientific explanations are contrastive. Further, frequently the point of a statistical explanation is to identify the etiology of its explanandum, but on standard models probabilistic explanations often fail to do so. This paper offers an alternative conception of statistical explanations on which explanations of the frequency of a property consist in the derivation of that frequency from a statistical specification of the mechanism by which instances of the relevant property are produced. Such explanations are contrastive precisely because they identify the determinate causal etiologies of their explananda.

From late 1684 through mid-1685, Isaac Newton turned to developing and refining the conceptual foundations presupposed by his emerging physics. Analysis of his manuscripts from this period reveals that Newton’s understanding of the relativity of acceleration led him to seek a spatiotemporally invariant quantity of matter. He found two such quantities and then designed an experiment to discover their relationship. Interpreting the experiment, however, required distinguishing a new notion of force. Others have recognized the conceptual distinction between inertial and gravitational mass. I show here that Newton clearly saw this distinction and that he provisionally established their equivalence.

The question is raised, what is going on when there is a tendency for something to happen, but it does not happen. A conspicuous case is that in which there is an equilibrium between opposing forces. One answer, given, for example, by Anscombe and Geach, Harré, and Bhaskar, is that such tendencies are in a sense real, though not manifest. The other answer, given, for example, by Braithwaite and Mackie, is that statements of tendencies are conditional statements about what is manifest but ones in which some antecedents are not specified. The first of these answers is criticized and the second defended.

This paper contrasts two information-theoretic approaches to statistical explanation: namely, (1) an analysis, which originated in my earlier research on problems of testing stochastic models of learning, based on an entropy-like measure of expected transmitted-information (and here referred to as the Expected-Information Model), and (2) the analysis, which was proposed by James Greeno (and which is closely related to Wesley Salmon's Statistical Relevance Model), based on the information-transmitted-by-a-system. The substantial differences between these analyses can be traced to the following basic difference. On Greeno's view, the essence of explanation lies in the relevance relations expressed by the conditional probabilities that relate the explanans variables to the explanandum variables; on my view, in contrast, the essence of explanation lies in theories viewed as hypothetical structures which deductively entail conditional probability distributions linking the explanans variables and the explanandum variables. The explanatory power of a stochastic theory is identified with information (regarding the values of explanandum variables) which is “absorbed from” the explanans variables. While other information which is “absorbed from” the explanandum variables (through the process of parameter estimation, for example) reflects descriptive power of the theory. I prove that Greeno's measure of transmitted information is a limiting special case of the E-I model, but that the former, unlike the latter, makes no distinction between explanatory power and descriptive power.

The paper, analyses the role of measurable concepts in deductive explanation. It is shown that such concepts are, although imprecise in a defined sense, exact in that neutral candidates to them do not arise. An analysis is given of the way in which imprecision is related to generalisation, and it is shown how imprecise concepts are incorporated in testable deductive explanations.

Theoretical problems concerning concepts of systems and measurement of behavior were encountered during experimental studies of the effects of electrical stimulation of the brain on the social behavior of a monkey colony. General problems involved in the description and measurement of behavior of natural systems, and especially of organisms are discussed.

In animals with differentiated brain the general process of stimulation may be divided into four subprocesses: input, throughput, transput and output.

Categories of behavior, temporal and spatial units, and logical rules of homogeneity for sorting data and comparing measurements and categories, are discussed. Simple behavioral units are divided into: 1. Individual a. static or postural b. dynamic or gestural 2. Social a. static b. dynamic. Complex behavior is divided into: 1. Simultaneous 2. Sequential 3. Syntactic 4. Roles.

The process of interpretation of data by the observer is assumed to be dependent upon actual behavior of the system, techniques of observation and experimentation, theoretical and technical processes of analysis, and synthesis and integration of data. Expression of behavior in mathematical terms insures greater accuracy than descriptive methods and enables data to be processed with computers.

The literary and poetic turn in philosophy exudes contempt for science and hostility against technology, both allegedly justified on grounds established by Nietzsche and Heidegger. I try to show that these grounds instead call for an extremely positive assessment of science and technology. It turns out that what Nietzsche and Heidegger describe as our highest achievement, namely, human autonomy, is really made possible by modern experimental physics. And I show how this assessment is borne out by what Heisenberg describes as the lesson of quantum physics.

In this paper I consider two objections raised by Nick Smith (1997) to an argument against the probability of time travel given by Paul Horwich (1995, 1987). Horwich argues that time travel leads to inexplicable and improbable coincidences. I argue that one of Smith's objections fails, but that another is correct. I also consider an instructive way to defend Horwich's argument against the second of Smith's objections, but show that it too fails. I conclude that unless there is something faulty in the conception of explanation implicit in Horwich's argument, time travel presents us with nothing that is inexplicable.