A number of areas of biology raise questions about what is of value in the natural environment and how we ought to behave towards it: conservation biology, environmental science, and ecology, to name a few. Based on my experience teaching students from these and similar majors, I argue that the field of environmental ethics has much to teach these students. They come to me with pent-up questions and a feeling that more is needed to fully engage in their subjects, and (...) I believe some exposure to environmental ethics can help focus their interests and goals. I identify three primary areas in which environmental ethics can con- tribute to their education. The first is an examination of who (or what) should be considered to be part of our moral community (i.e., the community to whom we owe direct duties). Is it humans only? Or does it include all sentient life? Or all life? Or ecosystems considered holistically? Often, readings implicitly assume one or more of these answers; the goal is to make the student more sensitive to these implicit claims and to get them to think about the different reasons that support them. The second area, related to the first, is the application of the different answers concerning the extent of the ethical community to real environmental issues and problems. Students need to be aware of how the different answers concerning the moral community can imply conflicting answers for how we should act in certain cases and to think about ways to move toward conflict resolution. The third area in which environmental ethics can contribute is a more conceptual one, focusing on central concepts such as biodiversity, sustainability, species, and ecosystems. Exploring and evaluating various meanings of these terms will make students more reflective and thoughtful citizens and biologists, sensitive to the implications that different conceptual choices make. (shrink)

The three cardinal aims of science are prediction, control, and explanation; but the greatest of these is explanation. Also the most inscrutable: prediction aims at truth, and control at happiness, and insofar as we have some independent grasp of these notions, we can evaluate science’s strategies of prediction and control from the outside. Explanation, by contrast, aims at scientific understanding, a good intrinsic to science and therefore something that it seems we can only look to science itself to explicate.

We compare the neo-Galtonian and nomothetic approaches of psychological research. While the former focuses on summarized statistics that depict average subjects, the latter focuses on general facts of form 'if conditions then restricted outcomes'. The nomothetic approach does not require quantification as a convenient way of statistical modeling. The nice feature of a general fact is its falsifiability by the observation of a single case. Hence, as a clear sense of scientific error is re-introduced in the research paradigm, we detail (...) two kinds of puzzle-solving: repairing general facts by contraction or by expansion of the initial conditions. This style of research does not require that researchers depend on highly skilled engineers in data analysis, as the very structure of a general fact can be established by scrutinizing a contingency table. (shrink)

This special issue aims to redress the balance and to open up Gaston Bachelard's work beyond a small in-crowd of experts and aficionado’s in France. It aims to stimulate the discovery of new and understudied aspects of Bachelard’s work, including aspects of the intellectual milieu he was working in. Fortunately, for this purpose we were able to rely both on renowned Bachelard specialists, such as Hans-Jörg Rheinberg-er, Cristina Chimisso and Dominique Lecourt, as well as on a number of younger scholars (...) who are discovering their work in a different intellectual context. At the same time we also want to reassess the value of this oeuvre, which also entails examining the reasons and causes of the relative neglect of Bachelard’s work in recent times. Has it exhausted its possibilities? Does it have intrinsic limitations that have contributed to the eclipse, as some influential, mainly French, philoso-phers have more or less explicitly suggested? (shrink)

The idea that a serious threat to scientific realism comes from unconceived alternatives has been proposed by van Fraassen, Sklar, Stanford and Wray among others. Peter Lipton's critique of this threat from underconsideration is examined briefly in terms of its logic and its applicability to the case of space-time and particle physics. The example of space-time and particle physics indicates a generic heuristic for quantitative sciences for constructing potentially serious cases of underdetermination, involving one-parameter family of rivals T_m that work (...) as a team rather than as a single rival against default theory T_0. In important examples this new parameter has a physical meaning and makes a crucial _conceptual_ difference, shrinking the symmetry group and in some case putting gauge freedom, formal indeterminism vs. determinism, the presence of the hole argument, etc., at risk. Methodologies akin to eliminative induction or tempered subjective Bayesianism are more demonstrably reliable than the custom of attending only to "our best theory": they can lead either to a serious rivalry or to improved arguments for the favorite theory. The example of General Relativity vs. massive spin 2 gravity, a recent topic in the physics literature, is discussed. Arguably the General Relativity and philosophy literatures have ignored the most serious rival to General Relativity. (shrink)

In the first half of this dissertation, I develop a heuristic methodology for analyzing scientific solutions to the problem of underdetermination. Heuristics are rough-and-ready procedures used by scientists to construct models, design experiments, interpret evidence, etc. But as powerful as they are, heuristics are also error-prone. Therefore, I argue that they key to prudently using a heuristic is the articulation of meta-heuristics---guidelines to the kinds of problems for which a heuristic is well- or ill-suited. Given that heuristics will introduce certain (...) errors into our scientific investigations, I emphasize the importance of a particular category of meta-heuristics involving the search for robust evidence. Robustness is understood to be the epistemic virtue bestowed by agreement amongst multiple modes of determination. The more modes we have at our disposal, and the more these confirm the same result, the more confident can we be that a result is not a mere artifact of some heuristic simplification. Through an analysis of case-studies in the philosophy of biology and clinical trials, I develop a principled method for modeling and evaluating heuristics and robustness claims in a qualitative problem space. The second half of the dissertation deploys the heuristic methodology to address ethical and epistemological issues in the science of clinical trials. To that end, I develop a network model for the problem space of clinical research, capable of representing the various kinds of experiments, epistemic relationships, and ethical justifications intrinsic to the domain. I then apply this model to ongoing research with the antibacterial agent, moxifloxacin, for the treatment of tuberculosis, tracking its development from initially successful and promising in vitro and animal studies to its disappointing and discordant performance across five human efficacy trials. Given this failure to find a robust result with moxifloxacin across animal and human studies, what should researchers now do? While my final analysis of this case does not definitively answer that question, I demonstrate how my methodology, unlike a statistical meta-analysis, helps to clarify the directions for further research. (shrink)

According to the no miracles argument, scientific realism provides the only satisfactory explanation of the predictive success of science. It is argued in the present article that a different explanatory strategy, based on the posit of strong limitations to the underdetermination of scientific theory building by the available empirical data, offers a more convincing understanding of scientific success.

Analogies often provide reasons for pursuing hypotheses or models. This is illustrated with a case study on the liquid drop model of the atomic nucleus. I criticise accounts in which analogies provide reasons for pursuit through epistemic support, proposing instead that analogies increase the value of learning the truth. I consider two accounts of this type: first, that analogies indicate potentials for theoretical unification; second, that analogies facilitate the transfer of already well-understood modelling frameworks to new domains. While the first (...) is plausible for some cases, only the second can account for the liquid drop case study. (shrink)