The fate of William Whewell’s four palætiological domains : a comparative study

In 1847, the British polymath William Whewell pointed out that the sciences for which he, in 1837, had coined the term “palætiological” have much in common and that they may reflect light upon each other by being treated together. This recommendation is here put into practice in a specific way, to wit, not by comparing the palaetiological sciences that Whewell distinguished himself but by comparing the general historical development of the scientific study of the four broad palætiological domains that he enumerated in 1847: the solar system, the Earth, its vegetable and animal creation, and man. For wide and various as their subjects are, it will be found that [the palætiological sciences] have all certain principles, maxims, and rules of procedure in common; and thus may reflect light upon each other by being treated together. William Whewell ( 1847 , 1, p. 640

Robots on spaceship earth: a theoretical plea for machine metaphors

Metaphors are inevitable core elements of the conceptual schemes that shape our thinking and behavior. Traditionally, nature is interpreted in terms of agential metaphors such as ghosts, gods, witches and angels. Science, in contrast, is characterized by contra-intuitive, mechanistic thinking and machine metaphors. Modern societies nevertheless remain, to a certain extent, in the grip of powerful agential tropes. It will be argued that they are one of the obstacles that stand in the way of both reaping the full benefits of modern science and of meeting two of the biggest challenges we have ever faced: overpopulation and climate change. Or, put differrently, they are one of the reasons why there is a problematic mismatch between our modern "Umwelt" and "niche"

Needed : an ethics and ideology for spaceship Earth

This target article summarizes our discourse on one of the major questions raised in our recent book “Evolution Science and Ethics in the Third Millennium,” namely, to what degree are traditional religious belief systems and modern secular ideologies capable of contributing to an evolutionarily based ethical framework that can guide humanity to higher levels of hominization within the context of a further progressing modernization and a sustainable planetary ecology? The article concludes with some reflections on the need for interfaith dialogue and the possibility of integrating traditional religious belief systems and modern secular ideologies into a long-term evolutionarily based ethical approach

Linguistic reference in science : problems and progress

The crucial role that mathematical notation systems have played in the success of the hard or mathematical sciences is well known and richly documented: the origin of the history of these sophisticated notation systems more or less coincides with the birth of modern science. The role of our linguistic notation systems (as applied to, or used in, the scientific study of nature), by contrast, is hardly documented at all, at least not in a systematic way. We distinguish between (metaphorical and non-metaphorical) meta-scientific terms and scientific terms and, as far as the latter is concerned, between methodology and content terms. It is the latter sort of terms that interest us here. Five different dysfunctions in the relationship between scientific linguistic tokens and their referents will be presented and illustrated: scientific terms or phrases can not only be imprecise, they can also be meaningless, indiscriminate, inapt and ambiguous. By correcting or alleviating such dysfunctions, our linguistic notation systems have, in the course of the past four centuries, become more refined and functional scientific tools. This simple, illustrated taxonomy is not only historically relevant, it may also help contemporaneous scientists to identify and avoid possible pitfalls, associated with the use of language in science

Did Dawkins recant his selfish gene argument against group selection?

In 2007, David S. Wilson and Edward O. Wilson (27) pointed out that, Richard Dawkins had admitted that, contrary to what he had claimed in his book The Selfish Gene (1976) (7), the idea that only the gene is a fundamental unit of selection cannot be used as an argument against the notion of group selection. This elicited a sharp denial from Dawkins (30), which was followed by an explanatory reply by Wilson and Wilson (33) and another vehement denial by Dawkins (34). I analyse the prehistory of this surprisingly complex and convoluted dispute and subsequently disentangle it. My conclusion is that much of it is based on a series of misunderstandings. First, Wilson’s and Wilson’s (27) original interpretation of Dawkins’ selfish gene argument was incorrect. Second, in their explanatory reply (33), they distinguished between two kinds of group selection: the idea that groups can be units of selection (theoretical group selection) and the idea that group selection plays a functional role in evolution (functional group selection). They clarified that their claim concerned theoretical group selection, not functional group selection. Third, that clarified claim was correct and not correct. It was incorrect because Dawkins has never explicitly acknowledged that he had erred by developing his selfish gene theory as an implicit argument against this kind of group selection. However, the distinction that he made, by 1978, between two kinds of unit of selection, replicators (genes) and vehicles (somas), does imply such an acknowledgment since it holds that groups can be units of selection (vehicles). In this important sense, Wilson’s and Wilson’s clarified claim (33) was correct. Fourth, Dawkins’ second denial (34) concerned functional group selection, not theoretical group selection

A simulated annealing optimization of audio features for drum classification

Current methods for the accurate recognition of instruments within music are based on discriminative data descriptors. These are features of the music fragment that capture the characteristics of the audio and suppress details that are redundant for the problem at hand. The extraction of such features from an audio signal requires the user to set certain parameters. We propose a method for optimizing the parameters for a particular task on the basis of the Simulated Annealing algorithm and Support Vector Machine classification. We show that using an optimized set of audio features improves the recognition accuracy of drum sounds in music fragments