Abstract
In physics, the borderline between pure science and technology is increasingly diffuse. Physics can be seen as technoscience, a merged scientific and technological enterprise. The notion of technoscience has emerged from studies in the philosophy of science and sociology of science, and also seems to arise quite naturally in discussions with practicing scientists and as an underpinning of actual scientific practices. Nanophysics, the activities of which are closely connected with the advancement of technology and where modelling and simulations are extensively used, is a natural place to test how the ideas contained in technoscience can be used to understand these central activities and how they are learned. The views of physicists, both experts and novices, working on modelling and simulation problems in nanophysics and nanotechnology are examined in this study using multidimensional methods, to discover their views on how knowledge in their research field is acquired, constructed and justified—and how novices are enculturated into these knowledge-construction processes. Additionally, attention is paid to the question of the skills that are needed and how these skills, alongside the views of modelling, develop as a novice becomes an expert. The need to understand these basic epistemological processes is quite apparent from the viewpoint of understanding science, as well as in terms of using this understanding to guide education. The results of the analysis strongly suggest that ideas characterising technoscience are also present in the practitioners’ views; the technoscientific view can thus be used to understand and support the poorly understood process in which novices are enculturated as researchers in the field.
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Notes
Numerous attempts have shown that it is impossible to give a definition encompassing all the features of models and modelling. Therefore the terms “model” and “modelling” are not defined in this article, which focuses on nanomodellers’ views on computer models and simulations. The starting point of the discussion is not a predefined conception of models and modelling, rather, identification of a model in the answers of the practitioners is an outcome (see notes 8 and 9).
The term ‘enculturation’ is used, instead of ‘socialization’ (see, e.g., Gagner 2007 and the references therein), since the study focuses on the evolving cognitive processes, especially the epistemological aspects of knowledge construction and learning. The social nature of the knowledge building processes is seen as potentially supporting the enculturation in the construction and justification of knowledge.
The merging of science and technology is easy to see, for example, in many cases of research awarded the Nobel Prize: consider, for example, John L. Hall and Theodor W. Hänsch’s contribution to the development of laser-based precision spectroscopy (2005) and Alexei A. Abrikosov, Vitaly L. Ginzburg and Anthony J. Leggett’s pioneering contributions to the theory of superconductors and superfluids (2003), which were technologically motivated. Moreover, scientific design and testing of experimental machines, accelerators and detectors is at the heart of “Big science” (see Baird 2004). For example, George Charpak’s work to invent and develop particle detectors, in particular the multiwire proportional chamber (Nobel Prize in 1992) was both scientific and technological. As well in nanoscience research awarded a Nobel prize it is impossible to say where physics changes to technology, as in the case of Albert Fert and Peter Grünberg’s discovery of Giant Magnetoresistance (Nobel Prize 2007) or Gerd Binnig and Heinrich Rohrer’s inventory work on scanning tunnelling microscopes (Nobel Prize 1986).
A more detailed account is given in Tala (2009).
For convenience, small scale is referred to here, although it is not only the length scale but quantum mechanical behaviour which defines nanoscience. Nanoscientists (for example, those interviewed in this study) emphasize that when speaking about nanoscale we should not imagine it as a purely geometrical and temporal scaling down; the physical events in these extremely small conditions no longer follow classical physical laws but rather are described by quantum mechanical ones. These events are typically dominated by particular interface effects and exhibit properties which result from a limited number of constituents.
This is the view of reality as nanophysics opens it up to us. When making progress in nanophysics, a working physicist must build on this view of “reality” (Fine 1986).
For example, in logical empiricism a quite direct one-to-one relation between the abstract models and reality is seen. Later, in semantic views (Giere 1988, 1999; Van Fraassen 1980) is discussed about’similarity’: only the similarity between certain features of a conceptual model and certain observable features of a material model, and sometimes also the similarity between certain features of a material model and those of phenomenon occurring outside the laboratory, can be fitted. Moreover, in more recent views on models and modelling (e.g. Morrison and Morgan 1999; Morrison 1999), models gain a functional and autonomous role.
Because the study aims to find out the views of practitioners, which may not be based on any explicit analysis of models and modelling, but, rather, are developed in the concrete practices of modelling, it is better not to commit to any narrow view to models or definition of a model here. Nevertheless in this study, when met, “a model” is identified as such, within the framework of the recent views on models and modelling mentioned in the previous footnote (Giere 1988, 1999; Morrison and Morgan 1999; Morrison 1999; Van Fraassen 1980).
The modelling of the informants is often called realistic because the objective is a realistic description of the physical regularities of a phenomenon in the nanoworld.
The favoured ab inito description among these scientists is “DFT calculations” basing on density functional theory.
For a nice comparison between the methods employed, see Vvedensky 2004.
An expert stated that a functional model typically also “includes very unphysical characteristics…: a typical [nonphysical] characteristic is that the energy is not conserved but is created or destroyed by itself in the system during the simulation run”(E). This particular unphysical feature is typical in the works to which the expert refers, and not plausible in many others (having other unphysical characteristics), as a novice pointed out after reading the analysis of this study.
Thus “you should know the direction you should go in and where you should look, but this is a positive thing. The negative thing, of course, is that you are maybe influenced by your concepts, basically by the concepts you have in mind. And you may ignore—just not notice—what is really important”(E).
The same expert talked about a colleague, who concentrates on the quantum mechanical calculations used as references in MD simulating, but who does not believe that we can acquire better understanding of the world with the aid of quantum mechanical descriptions. As the expert said, this is unusual, but possible: to use a theory, one does not need to personally believe in its power (cf. Arthur Fine’s idea of working physicist’s motivational view 1986).
The views develop. The youngest novice can be described as a novice worker who carries out the job given by given black-boxes, but can neither reason the project nor the methods and models they use. Some novices can say something about the basis of “the black-box” they use. Finally, the most practiced novice uses two parallel methods quite independently, is able to report and estimate the quality of the basis of the models (s)he employs and even to develop a model by himself.
In fact, a computer is a kind of black-box for the expert developers of the modelling programs as well; experts say that the computer always brings characteristics into the process which even the developers did not know beforehand. The regularities of a phenomenon studied by a computer simulation are also regularities of a technological system. Since a model oscillates within the space delimited by its physics, it is called an epistemic object. Its black-box features are produced by the technological nature (see Latour 1987, Merz 1999). In the enculturation process, novices learn to live with this dual nature.
In reflective exercises, they consider what practical principles and views guide their own and their group’s work on knowledge construction and what principles and views guide the construction and justification of models in their field in general. In this reflective process, students have to compare the views of their field with those of other fields, as well as the styles and views of their group with the styles and views of other groups. They may try to find out how their views are similar and in what respects they may differ from those of their supervisors and colleagues, and from those of international colleagues they have worked with.
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Acknowledgments
I would like to thank the participant in this study and express my appreciation for many useful discussions with Dr Ismo Koponen and Dr Tarja Knuuttila concerning the topic and form of the present work.
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Appendices
Appendix: The Questionnaire Translated into English
1.1 About the Research
The philosophy of science and technology addresses the problem of the principles of the construction and justification of knowledge. These principles guide such things as how to prove that a model operates satisfactorily and how to convince others. They are field-specific and are rarely discussed explicitly. Understanding these principles helps novices to learn the field and finally become experts. Although understanding this kind of knowledge and how it is acquired is important from the viewpoint of educational research and the development of education, it rarely been researched in its context. The philosophical notions which articulate and analyse the views of practicing researchers assist in understanding the methods and contents. In this research project, we will map the views of selected nanophysicists who work in one way or another with computer simulations in order to find out how knowledge is seen to be constructed and justified in nanophysics practices.
I would like to ask you the following questions to obtain out your views about these issues. Please, send me a brief written answer to each one, which will help me to prepare complementary questions for the interview. I also ask that you send me two or three of your particularly good publications or presentations concerning simulations as well as a brief account of why you consider these to be the best. If you give examples of your other projects in your responses, please send me the relevant publications as well.
Objectives
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1.
What is the research frame and objective of your research? (For example, what is modelled and what kind of empirical results do you wish to understand?) If you have several projects, please mention them all. Concentrating on projects connected to the nanoworld and modelling would be appreciated.
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2.
Please describe your research group and your role in it.
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3.
Considered from a broader viewpoint, to what subject matter is your research project(s) related? What is the significance of the projects? (In answering this, please briefly describe how you perceive the research field you are involved in.)
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4.
How would you explain the importance of your research field to the public or to funding bodies?
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5.
How would you explain the importance of your research field to a physicist from another field?
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6.
How would you explain the importance of your research field to a physics student interested in the field?
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7.
Please sketch out the methods you use.
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8.
What general problem-solving skills and modelling skills do you employ or achieve in your work or what can a working student in your project can achieve? How are these skills related to the methods you use?
Relation to Reality
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1.
What models or kinds of models are central to your project? In which models’ and simulations’ development do you and your research group have a central role?
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2.
What is the role of simulation in your research project? (For example, what is the relation between simulation and models? Do you develop new models or employ already developed ones?)
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3.
How would you characterize the relation between the models you use, the theory, and the empirical results? (For example, do you derive the models from the theories or are they constructed from empirical results? How are they developed further?)
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4.
You may like to talk here about the differences between model and theory in your terminology.
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5.
In what respect does the central model you use represent reality? (In what respect does it not?) Please give examples of the models of different types.
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6.
In what respect does the central model you use, relate to the theories already established? (In what respect does it not?) Please, give examples of the models of different types.
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7.
In what respects are the central models you have developed or use in line with the established theoretical knowledge and in what respects are they not? (Why?) Please, give different kinds of examples.
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8.
In what respects are the simulations you have developed or use in line with the established theoretical knowledge and in what respects are they not? (Why?) Please, give different kinds of examples.
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9.
If the models or simulations you employ or have developed include characteristics which differ from those of the real systems, why do you think these idealizations or approximations have been created? Please, give different examples and reasons.
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10.
How could the model or simulation be changed if we had more effective computers or technological development was more advanced in some other way?
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11.
How would you describe the way your work advances knowledge and understanding in your research field?
Functionality (from the viewpoint of convincing researchers)
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What characteristics make the models you have developed or use important or interesting?
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What increases your confidence and how do you increase the other researchers’ confidence in the functionality and reliability of a model or a simulation method you employ?
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3.
How do you convince other researchers in your field that your interpretations of the results from the model or simulation are correct?
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4.
What other means are used to make sure that the simulations function and the results have been interpreted in the right way in your research area?
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5.
What are the objectives of your or your postgraduates’ researcher education? (For example, having completed this education, what should you/they be able to do, what skills should you/they have and to what kind of tasks should you/they be able to apply these skills?)
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Tala, S. Enculturation into Technoscience: Analysis of the Views of Novices and Experts on Modelling and Learning in Nanophysics. Sci & Educ 20, 733–760 (2011). https://doi.org/10.1007/s11191-010-9277-4
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DOI: https://doi.org/10.1007/s11191-010-9277-4