Quantitative genetics (QG) analyses variation in traits of humans, other animals, or plants in ways that take account of the genealogical relatedness of the individuals whose traits are observed. “Classical” QG, where the analysis of variation does not involve data on measurable genetic or environmental entities or factors, is reformulated in this article using models that are free of hypothetical, idealized versions of such factors, while still allowing for defined degrees of relatedness among kinds of individuals or “varieties.” The gene (...) - free formulation encompasses situations encountered in human QG as well as in agricultural QG. This formulation is used to describe three standard assumptions involved in classical QG and provide plausible alternatives. Several concerns about the partitioning of trait variation into components and its interpretation, most of which have a long history of debate, are discussed in light of the gene-free formulation and alternative assumptions. That discussion is at a theoretical level, not dependent on empirical data in any particular situation. Additional lines of work to put the gene-free formulation and alternative assumptions into practice and to assess their empirical consequences are noted, but lie beyond the scope of this article. The three standard QG assumptions examined are: (1) partitioning of trait variation into components requires models of hypothetical, idealized genes with simple Mendelian inheritance and direct contributions to the trait; (2) all other things being equal, similarity in traits for relatives is proportional to the fraction shared by the relatives of all the genes that vary in the population (e.g., fraternal or dizygotic twins share half of the variable genes that identical or monozygotic twins share); (3) in analyses of human data, genotype-environment interaction variance (in the classical QG sense) can be discounted. The concerns about the partitioning of trait variation discussed include: the distinction between traits and underlying measurable factors; the possible heterogeneity in factors underlying the development of a trait; the kinds of data needed to estimate key empirical parameters; and interpretations based on contributions of hypothetical genes; as well as, in human studies, the labeling of residual variance as a non-shared environmental effect; and the importance of estimating interaction variance. (shrink)
Despite a long history of debates about the heritability of human traits by researchers and other critical commentators, the possible heterogeneity of genetic and environmental factors that underlie patterns in observed traits has not been recognized as a significant conceptual and methodological issue. This article is structured to stimulate a wide range of readers to pursue diverse implications of underlying heterogeneity and of its absence from previous debates. Section 1, a condensed critique of previous conceptualizations and interpretations of heritability studies, (...) consists of three core propositions centered on heterogeneity and six supplementary propositions. Reference is made to agricultural evaluation trials in which a number of different genetically replicable varieties are raised in multiple replicates in one or more locations. In such analyses, the best case for illuminating genetic and environmental factors can be achieved; analyses in human genetics, in contrast, fall far short of the ideal. Section 2 identifies a wide range of questions that invite philosophical, historical, sociological, and scientific inquiry. These are organized under four headings: debate over the conceptual implications of heterogeneity; history of translation of methods from agriculture and laboratory breeding into human genetic analysis; racialized imaginaries in the analysis of differences among groups; and areas of scientific inquiry that may allow more attention to underlying heterogeneity. (shrink)
Using data on the ‘career’ paths of one thousand ‘leading scientists’ from 1450 to 1900, what is conventionally called the ‘rise of modern science’ is mapped as a changing geography of scientific practice in urban networks. Four distinctive networks of scientific practice are identified. A primate network centred on Padua and central and northern Italy in the sixteenth century expands across the Alps to become a polycentric network in the seventeenth century, which in turn dissipates into a weak polycentric network (...) in the eighteenth century. The nineteenth century marks a huge change of scale as a primate network centred on Berlin and dominated by German-speaking universities. These geographies are interpreted as core-producing processes in Wallerstein’s modern world-system; the rise of modern scientific practice is central to the development of structures of knowledge that relate to, but do not mirror, material changes in the system. (shrink)
Ambitiously identifying fresh issues in the study of complex systems, Peter J. Taylor, in a model of interdisciplinary exploration, makes these concerns accessible to scholars in the fields of ecology, environmental science, and science studies. Unruly Complexity explores concepts used to deal with complexity in three realms: ecology and socio-environmental change; the collective constitution of knowledge; and the interpretations of science as they influence subsequent research. For each realm Taylor shows that unruly complexity-situations that lack definite boundaries, where what (...) goes on "outside" continually restructures what is "inside," and where diverse processes come together to produce change-should not be suppressed by partitioning complexity into well-bounded systems that can be studied or managed from an outside vantage point. Using case studies from Australia, North America, and Africa, he encourages readers to be troubled by conventional boundaries-especially between science and the interpretation of science-and to reflect more self-consciously on the conceptual and practical choices researchers make. (shrink)
I characterize and then complicate Solomon, Thagard and Goldman's framing of the issue of integrating cognitive and social factors in explaining science. I sketch a radically (...) class='Hi'>different framing which distributes the mind beyond the brain, embodies it, and has that mind-body-person become, as s/he always is, an agent acting in a society. I also find problems in Solomon's construal of multivariate statistics, Thagard's analogies for multivariate analysis, and Goldman's faith in the capacity of the community of users of scientific method to home in on true beliefs. (shrink)
Diagrams refer to the phenomena overtly represented, to analogous phenomena, and to previous pictures and their graphic conventions. The diagrams of ecologists Clarke, Hutchinson, and H.T. Odum reveal their search for physical analogies, building on the success of World War II science and the promise of cybernetics. H.T. Odum's energy circuit diagrams reveal also his aspirations for a universal and natural means of reducing complexity to guide the management of diverse ecological and social systems. Graphic conventions concerning framing and translation (...) of ecological processes onto the flat printed page facilitate Odum's ability to act as if ecological relations were decomposable into systems and could be managed by analysts external to the system. (shrink)
Ecologists grapple with complex, changing situations. Historians, sociologists and philosophers studying the construction of science likewise attempt to account for (or discount) a wide variety of influences making up the scientists' "ecologies of knowledge." This paper introduces a graphic methodology, mapping, designed to assist researchers at both levels-in science and in science studies-to work with the complexity of their material. By analyzing the implications and limitations of mapping, I aim to contribute to an ecological approach to the philosophy of science.