This article presents a new framework for the analysis of experimental control. The framework highlights different functions for experimental controls in the realization of an experiment: experimental controls that serve as tests and experimental controls that serve as probes. The approach to experimental control proposed here can illuminate the constitutive role of controls in knowledge production, and it sheds new light on the notion of exploratory experimentation. It also clarifies what can and what cannot be expected from reviewers of scientific journal articles giving feedback on experimental controls.

In the human sciences, experimental research is used to establish causal relationships. However, the extrapolation of these results to the target population can be problematic. To facilitate extrapolation, we propose to use the statistical technique Latent Class Regression Analysis in combination with the analogical reasoning theory for extrapolation. This statistical technique can identify latent classes that differ in the effect of X on Y. In order to extrapolate by means of analogical reasoning, one can characterize the latent classes by a combination of features and then compare these features to features of the target.

Merging of opinions results underwrite Bayesian rejoinders to complaints about the subjective nature of personal probability. Such results establish that sufficiently similar priors achieve consensus in the long run when fed the same increasing stream of evidence. Here, we establish a merging result for sets of probability measures updated by Jeffrey conditioning. This generalizes a number of different merging results in the literature. We also show that such sets converge to a shared, maximally informed opinion. Finally, we demonstrate the philosophical significance of our study by detailing applications to the topics of dynamic coherence, imprecise probabilities, and opinion pooling.

Many philosophers have challenged the ideal of value-free science on the grounds that social or moral values are relevant to inferential thresholds. But given this view, how precisely and to what extent should scientists adjust their inferential thresholds in light of nonepistemic values? We suggest that signal detection theory provides a useful framework for addressing this question. Moreover, this approach opens up further avenues for philosophical inquiry and has important implications for philosophical debates concerning inductive risk. For example, the signal detection theory framework entails that considerations of inductive risk and inferential-threshold placement cannot be conducted in isolation from base-rate information.

Shuford, Albert, and Massengill proved, a half century ago, that the logarithmic scoring rule is the only proper measure of inaccuracy determined by a differentiable function of probability assigned the actual cell of a scored partition. In spite of this, the log rule has gained less traction in applied disciplines and among formal epistemologists that one might expect. In this article we show that the differentiability criterion in the Shuford et al. result is unnecessary and use the resulting simplified characterization of the logarithmic rule to give novel arguments in favor of it.

According to mainstream linguistic phonetics, speech can be modeled as a string of discrete sound segments or “phones” drawn from a universal phonetic inventory. Recent work has argued that a mature phonetics should refrain from theorizing about speech and speech processing using sound segments and that the phone concept should be eliminated from linguistic theory. The article lays out the tenets of the phone methodology and evaluates its prospects in light of the eliminativist arguments. I claim that the eliminativist arguments fail to show that the phone concept should be eliminated from linguistic theory.

Philosophers of science discuss whether theory selection depends on aesthetic judgments or criteria and whether these putatively aesthetic features are genuinely extra-epistemic. As examples, judgments involving criteria such as simplicity and symmetry are often cited. However, other theory selection criteria, such as fecundity, coherence, internal consistency, and fertility, more closely match those criteria used in art contexts and by scholars working in aesthetics. Paying closer attention to the way these criteria are used in art contexts allows us to understand some evaluative and developmental practices in scientific theory selection as genuinely aesthetic, enlarging the scope of the goals of science.

This article argues for a representational semantic conception (RSC) of scientific theories, which respects the bare claim of any semantic view, namely, that theories can be characterized as sets of models. RSC must be sharply distinguished from structural versions that assume a further identity of ‘models’ and ‘structures’, which we reject. The practice turn in the recent philosophical literature suggests instead that modeling must be understood in a deflationary spirit, in terms of the diverse representational practices in the sciences. These insights are applied to some mathematical models, thus showing that the mathematical sciences are not in principle counterexamples to RSC.

Timothy Williamson claimed to prove with a coin-tossing example that hyperreal probabilities cannot save the principle of regularity. A premise of his argument is that two specified infinitary events must be assigned the same probability because, he claims, they are isomorphic. But as has been pointed out, they are not isomorphic. A way of framing Williamson’s argument that does not make it depend on the isomorphism claim is in terms of shifts in Bernoulli processes, the usual mathematical model of sequential coin tossing. But even so framed, the argument still fails.