追踪物理学中的跨领域模因

以美国物理学会旗下期刊2000—2019年发表的论文和Web of Science论文摘要为基础,用模因短语刻画知识,构建模因关系网络并引入跨学科测度Rao-Stirling指数以计算模因的跨领域分数,从而追踪物理学中的跨领域模因。分别从网络拓扑结构指标、跨领域测度指标和专业术语对比3个方面进行验证,证明了所提的模因关系网络和模因的跨领域分数可以有效反映知识在不同领域间的扩散现象。     

Extensional scientific realism vs. intensional scientific realism

Extensional scientific realism is the view that each believable scientific theory is supported by the unique first-order evidence for it and that if we want to believe that it is true, we should rely on its unique first-order evidence. In contrast, intensional scientific realism is the view that all believable scientific theories have a common feature and that we should rely on it to determine whether a theory is believable or not. Fitzpatrick argues that extensional realism is immune, while intensional realism is not, to the pessimistic induction. I reply that if extensional realism overcomes the pessimistic induction at all, that is because it implicitly relies on the theoretical resource of intensional realism. I also argue that extensional realism, by nature, cannot embed a criterion for distinguishing between believable and unbelievable theories.     

Can the behavioral sciences self-correct? A social epistemic study

Advocates of the self-corrective thesis argue that scientific method will refute false theories and find closer approximations to the truth in the long run. I discuss a contemporary interpretation of this thesis in terms of frequentist statistics in the context of the behavioral sciences. First, I identify experimental replications and systematic aggregation of evidence (meta-analysis) as the self-corrective mechanism. Then, I present a computer simulation study of scientific communities that implement this mechanism to argue that frequentist statistics may converge upon a correct estimate or not depending on the social structure of the community that uses it. Based on this study, I argue that methodological explanations of the “replicability crisis” in psychology are limited and propose an alternative explanation in terms of biases. Finally, I conclude suggesting that scientific self-correction should be understood as an interaction effect between inference methods and social structures.     

Representing with imaginary models: Formats matter

Models such as the simple pendulum, isolated populations, and perfectly rational agents, play a central role in theorising. It is now widely acknowledged that a study of scientific representation should focus on the role of such imaginary entities in scientists’ reasoning. However, the question is most of the time cast as follows: How can fictional or abstract entities represent the phenomena? In this paper, I show that this question is not well posed. First, I clarify the notion of representation, and I emphasise the importance of what I call the “format” of a representation for the inferences agents can draw from it. Then, I show that the very same model can be presented under different formats, which do not enable scientists to perform the same inferences. Assuming that the main function of a representation is to allow one to draw predictions and explanations of the phenomena by reasoning with it, I conclude that imaginary models in abstracto are not used as representations: scientists always reason with formatted representations. Therefore, the problem of scientific representation does not lie in the relationship of imaginary entities with real systems. One should rather focus on the variety of the formats that are used in scientific practice.     

Mathematical formalisms in scientific practice: From denotation to model-based representation

The present paper argues that ‘mature mathematical formalisms’ play a central role in achieving representation via scientific models. A close discussion of two contemporary accounts of how mathematical models apply—the DDI account (according to which representation depends on the successful interplay of denotation, demonstration and interpretation) and the ‘matching model’ account—reveals shortcomings of each, which, it is argued, suggests that scientific representation may be ineliminably heterogeneous in character. In order to achieve a degree of unification that is compatible with successful representation, scientists often rely on the existence of a ‘mature mathematical formalism’, where the latter refers to a—mathematically formulated and physically interpreted—notational system of locally applicable rules that derive from (but need not be reducible to) fundamental theory. As mathematical formalisms undergo a process of elaboration, enrichment, and entrenchment, they come to embody theoretical, ontological, and methodological commitments and assumptions. Since these are enshrined in the formalism itself, they are no longer readily obvious to either the novice or the proficient user. At the same time as formalisms constrain what may be represented, they also function as inferential and interpretative resources.     

A different kind of revolutionary change: transformation from object to process concepts

I propose a new perspective with which to understand scientific revolutions. This is a conversion from an object-only perspective to one that properly treats object and process concepts as distinct kinds. I begin with a re-examination of the Copernican revolution. Recent findings from the history of astronomy suggest that the Copernican revolution was a move from a conceptual framework built around an object concept to one built around a process concept. Drawing from studies in the cognitive sciences, I then show that process concepts are independent of object concepts, grounded in specific regions of the brain and involving unique representational mechanisms. There are cognitive obstacles to the transformation from object to process concepts, and an object bias—a tendency to treat processes as objects—makes this kind of conceptual change difficult. Consequently, transformation from object to process concepts is disruptive and revolutionary. Finally, I explore the implications of this new perspective on scientific revolutions for both the history and philosophy of science.     

Silent performances: Are “repertoires” really post-Kuhnian?

Ankeny and Leonelli (2016) propose “repertoires” as a new way to understand the stability of certain research programs as well as scientific change in general. By bringing a more complete range of social, material, and epistemic elements into one framework, they position their work as a correction to the Kuhnian impulse in philosophy of science and other areas of science studies. I argue that this “post-Kuhnian” move is not complete, and that repertoires maintain an internalist perspective. Comparison with an alternative framework, the “sociotechnical imaginaries” of Jasanoff and Kim (2015), illustrates precisely which elements of practice are externalized by Ankeny and Leonelli. Specifically, repertoires discount the role of audience, without whom the repertoires of science are unintelligible, and lack an explicit place for ethical and political imagination, which provide meaning for otherwise mechanical promotion of particular research programs. This comparison reveals, I suggest, two distinct modes of scholarship, one internalist and the other critical. While repertoires can be modified to meet the needs of critical STS scholars and to completely reject Kuhn's internalism, whether or not we do so depends on what we want our scholarship to achieve.     

Ostracod fauna of salt Lake Acıgöl (Acı Tuz) (Turkey)

Lakes and wetlands in Mediterranean regions are overexploited and contaminated without sufficient local governmental control. The biodiversity of Salt Lake Ac?göl (Turkey), an internationally recognized ecosystem, is presently endangered by management plans. This study aims to analyse the ostracod community to establish its ecological status as a base for future paleoecological studies using ostracods to reconstruct the recent evolution of this wetland. Samples were collected seasonally and 13 species were recorded. Multivariate classification and ordination statistical methods revealed a major difference between assemblages related to the relative abundance of species tolerant of desiccation vs. species preferring permanent waters. Cyprideis torosa, mainly found in saline, low diversity sites, is a euryhaline species regarded as an indicator of future lake conditions if inappropriate water resource management continues.     

Soil oribatid mite (Acari: Oribatida) diversity and composition in semi-deciduous forest fragments in eastern Amazonia and comparison with the surrounding savanna matrix

We recorded species abundance and richness of oribatid mites along 16 plots established in semi-deciduous forest fragments in Amazonia. The results were compared with a published dataset consisting of an inventory carried out in 38 plots in the surrounding savanna. Totals of 143 and 91 species were recorded in the forest fragments and savanna, respectively. Sørensen similarity index between both environments was 0.44. Ordination of sites according to oribatid mite species composition showed a clear separation between forest fragments and savanna. Rostrozetes ovulum, Archegozetes longisetosus and Eohypochthonius (Eohypochthonius) becki were abundant and frequent in the forest fragments but exceedingly rare in the savanna. Neoppia (Neoppia) schauenbergi, Pseudoppia sp. C, Microppia sp. A and Cosmochthonius sp. A were limited to the savanna. This study also represents an early step toward knowing which groups of species are exclusive to one or another vegetation type or are sensitive to their inherent environmental conditions.     

Collaborative explanation and biological mechanisms

This paper motivates and outlines a new account of scientific explanation, which I term ‘collaborative explanation.’ My approach is pluralist: I do not claim that all scientific explanations are collaborative, but only that some important scientific explanations are—notably those of complex organic processes like development. Collaborative explanation is closely related to what philosophers of biology term ‘mechanistic explanation’ (e.g., Machamer et al., Craver, 2007). I begin with minimal conditions for mechanisms: complexity, causality, and multilevel structure. Different accounts of mechanistic explanation interpret and prioritize these conditions in different ways. This framework reveals two distinct varieties of mechanistic explanation: causal and constitutive. The two have heretofore been conflated, with philosophical discussion focusing on the former. This paper addresses the imbalance, using a case study of modeling practices in Systems Biology to reveals key features of constitutive mechanistic explanation. I then propose an analysis of this variety of mechanistic explanation, in terms of collaborative concepts, and sketch the outlines of a general theory of collaborative explanation. I conclude with some reflections on the connection between this variety of explanation and social aspects of scientific practice.