Diversity Reading List

Abstract: Why do some epistemic objects persist despite undergoing serious changes, while others go extinct in similar situations? Scientists have often been careless in deciding which epistemic objects to retain and which ones to eliminate; historians and philosophers of science have been on the whole much too unreflective in accepting the scientists' decisions in this regard. Through a re-examination of the history of oxygen and phlogiston, I will illustrate the benefits to be gained from challenging and disturbing the commonly accepted continuities and discontinuities in the lives of epistemic objects. I will also outline two key consequences of such re-thinking. First, a fresh view on the (dis)continuities in key epistemic objects is apt to lead to informative revisions in recognized periods and trends in the history of science. Second, recognizing sources of continuity leads to a sympathetic view on extinct objects, which in turn problematizes the common monistic tendency in science and philosophy; this epistemological reorientation allows room for more pluralism in scientific practice itself.

Abstract: Philosophers of experiment have acknowledged that experiments are often more than mere hypothesis-tests, once thought to be an experiment's exclusive calling. Drawing on examples from contemporary biology, I make an additional amendment to our understanding of experiment by examining the way that `wide' instrumentation can, for reasons of efficiency, lead scientists away from traditional hypothesis-directed methods of experimentation and towards exploratory methods.

Abstract: I propose a framework that explicates and distinguishes the epistemic roles of data and models within empirical inquiry through consideration of their use in scientific practice. After arguing that Suppes' characterization of data models falls short in this respect, I discuss a case of data processing within exploratory research in plant phenotyping and use it to highlight the difference between practices aimed to make data usable as evidence and practices aimed to use data to represent a specific phenomenon. I then argue that whether a set of objects functions as data or models does not depend on intrinsic differences in their physical properties, level of abstraction or the degree of human intervention involved in generating them, but rather on their distinctive roles towards identifying and characterizing the targets of investigation. The paper thus proposes a characterization of data models that builds on Suppes' attention to data practices, without however needing to posit a fixed hierarchy of data and models or a highly exclusionary definition of data models as statistical constructs.

Summary: Okhrulik offers a feminist critique of biology, a "real" science, to show that it is not just the "soft" social sciences that are affected by bias. She argues that preconceptions can interfere not only in cases of "bad science", but even when the rules of scientific practice are followed. There is no safeguard against the effects of bias in the context of discovery. Even if theories are rigorously tested to remove bias, some theories might not even be generated and so would not get to the point of being counted as competitors in the testing stage. This is illustrated by a number of case studies. Okhrulik concludes that a diversity of viewpoints is crucial.