Complex life cycles are common in the eukaryotic world, and they complicate the question of how to define individuality. Using a bottom-up, gene-centric approach, I consider the concept of fitness in the context of complex life cycles. I analyze the fitness effects of an allele (or a trait) on different biological units within a complex life history and how these effects drive evolutionary change within populations. Based on these effects, I attempt to construct a concept of fitness that accurately predicts evolutionary change in the context of complex life cycles.

This paper proposes an account of scientific data that makes sense of recent debates on data-driven research, while also building on the history of data production and use particularly within biology. In this view, 'data' is a relational category applied to research outputs that are taken, at specific moments of inquiry, to provide evidence for knowledge claims of interest to the researchers involved. They do not have truth-value in and of themselves, nor can they be seen as straightforward representations of given phenomena. Rather, they are fungible objects defined by their portability and their prospective usefulness as evidence.

Using Gebharter's (2014) representation, we consider aspects of the problem of discovering the structure of unmeasured sub-mechanisms when the variables in those sub-mechanisms have not been measured. Exploiting an early insight of Sober's (1998), we provide a correct algorithm for identifying latent, endogenous structure-sub-mechanisms-for a restricted class of structures. The algorithm can be merged with other methods for discovering causal relations among unmeasured variables, and feedback relations between measured variables and unobserved causes can sometimes be learned.

Characterizing the genetic structure of populations is of importance to evolutionary biology, to human disease gene mapping and to forensic science. Sewall Wright introduced a set of "F-statistics" to describe population structure in 1951 and he emphasized that these quantities were ratios of variances. Responding to uncertainty over the best way to estimate F-statistics, Weir and Cockerham published a method-of-moments set of estimators in 1984 (Evolution 38:1358-1370). This paper continues to be widely cited, with over 7,000 citations to date. Some background to the publishing history of the Weir and Ccckerham paper is given here, along with subsequent developments and a discussion of current uses of Wright's F-statistics.

Developmental systems theory (DST) is often dismissed on the basis that the causal indispensability of nongenetic factors in evolution and development has long been appreciated. A reformulation makes a more substantive claim: that the special role played by genes is also played by some (but not all) nongenetic resources. That special role can be captured by Shea's 'inherited representation'. Formulating DST as the claim that there are nongenetic inherited representations turns it into a striking, empirically testable hypothesis. DST's characteristic rejection of a gene versus environment dichotomy is preserved but without dissolving into an interactionist casual soup, as some have alleged.

When scientific research collides with social values, science's right to self-governance becomes an issue of paramount concern. In this article, I develop an account of scientific autonomy within a framework of public oversight. I argue that scientific autonomy is justified because it promotes the progress of science, which benefits society, but that restrictions on autonomy can also be justified to prevent harm to people, society, or the environment, and to encourage beneficial research. I also distinguish between different ways of limiting scientific autonomy, and I argue that government involvement in scientific decision-making should usually occur through policies that control the process of science, rather than policies that control the content of science.

Race is a prominent category in medicine. Epidemiologists describe how rates of morbidity and mortality vary with race, and doctors consider the race of their patients when deciding whether to test them for sickle-cell anemia or what drug to use to treat their hypertension. At the same time, critics of racial classification say that race is not real but only an illusion or that race is scientifically meaningless. In this paper, I explain how race is used in medicine as a proxy for genes that encode drug metabolizing enzymes and how a proper understanding of race calls into doubt the practice of treating race as a marker of any medically relevant genetic trait.

This paper questions the prevailing historical understanding that scientific racism "retreated" in the 1950s when anthropology adopted the concepts and methods of population genetics and race was recognized to be a social construct and replaced by the concept of population. More accurately, a "populational" concept of race was substituted for a "typological one"--this is demonstrated by looking at the work of Theodosius Dobzhansky circa 1950. The potential for contemporary research in human population genetics to contribute to racism needs to be considered with respect to the ability of the typological-population distinction to arbitrate boundaries between racist society and nonracist, even anti-racist, science. I point out some ethical limits of "population thinking" in doing so.