This paper explores the social dynamics of so-called intercomparison projects. An intercomparison project is a type of collaborative project that takes place in a number of simulation-based research areas such as astrophysics and climate modelling. Intercomparison projects can be seen as form of metrological practice in which the participants compare the results of numerical simulations of the ‘same’ scientific problem in order to ensure their reliability and validity. The paper is based on case studies of astrophysics, meteorology and oceanography, and the focus is on the organization and coordination of intercomparison projects. I argue that such projects have the effect of defining which scientists work on a particular problem and that they also serve as organizational vehicles for creating and presenting a dominant view of — and a standard result for — that problem. These types of projects are important for understanding numerical simulation-based research, because they show that expectations about desirable results are generated within the group.
Agrawala S. ( 1998) Context and early origins of the Intergovernmental Panel on Climate Change. Climatic Change39: 605-620.
2.
Aspers P. ( 2005) ‘Identitetsformation i gränssnitt’ (Identity formation in interfaces). Distinktion: Scandinavian Journal of Social Theory11: 41-57.
3.
Cole S. ( 1970) Professional standing and the reception of scientific discoveries. American Journal of Sociology76(2): 286-306.
4.
Collins HM ( 1985) Changing Order: Replication and Induction in Scientific Practice. London: SAGE Publications .
5.
Collins HM ( 1999) Tantalus and the aliens: Publications, audiences and the search for gravitational waves. Social Studies of Science29(2): 163-197.
6.
Edwards PN ( 2006) Meteorology as infrastructural globalism. OSIRIS21: 229-250.
7.
Fujimura J. ( 1987) ‘Constructing "do-able" problems in cancer research: Articulating alignment. Social Studies of Science17(2): 257-293.
8.
Global Energy and Water Cycle Experiment ( 2000) GEWEX Cloud System Study. Second Science and Implementation Plan. IGPO Publication Series, no. 34.
9.
Gubrium JF and Holstein JA ( 1997) The New Language of Qualitative Method. Oxford: Oxford University Press.
10.
Harper KC ( 2003) Research from the boundary-layer: Civilian leadership, military funding and the development of numerical weather prediction (1946-1955) . Social Studies of Science33(5): 667-696.
11.
Kintish E. ( 2008) Climate science: Turbulent times for climate model. Science321(5892):1032-1034.
12.
Lahsen M. ( 2005) Seductive simulations? Uncertainty distribution around climate models. Social Studies of Science35(6): 895-922.
13.
Latour B. ( 1987) Science in Action: How to Follow Scientists and Engineers through Society. Cambridge: Harvard University Press.
14.
Lynch M and Woolgar S (eds) (1990) Representation in Scientific Practice (Cambridge, MA: MIT Press ).
15.
Mallard A. ( 1998) Compare, standardize and settle agreement: On some usual metrological problems. Social Studies of Science28(4): 571-601.
16.
Merry SE ( 1984) Rethinking gossip and scandal. In: Black D (ed.) Toward a General Theory of Social Control, vol. II. New York: Academic Press, 271-302.
17.
Merton RK ( 1968) The Matthew effect in science. Science159(3810): 56-63.
18.
Miller CA and Edwards PN (eds) (2001) Changing the Atmosphere: Expert Knowledge and Environmental Governance. Cambridge, MA: MIT Press.
19.
O’Connell, Joseph ( 1993) Metrology: The creation of universality by the circulation of particulars. Social Studies of Science23(1): 129-173.
20.
Oreskes N., Shrader-Freshette K. and Belitz K. ( 1994) Verification, validation, and confirmation of numerical models in the earth sciences. Science263 (4 February): 641-646.
21.
Podolny JM ( 2005) Status Signals: A Sociological Study of Market Competition . Princeton: Princeton University Press.
22.
Schaffer S. ( 1992) Late Victorian metrology and its instrumentation: A manufactory of ohms. In: Bud R and Cozzens S (eds) Invisible Connections: Instruments, Institutions and Science. Bellingham, WA: Optical Engineering Press, 23-56.
23.
Shackley S., Risbey J., Stone P. and Wynne B. ( 1999) Adjusting to policy expectations in climate change modelling: An interdisciplinary study of flux adjustments in coupled atmosphere-ocean GCMs. Climatic Change43: 413-454.
24.
Shrum W., Genuth J. and Chompalov I. ( 2007) Structures of Scientific Collaboration. Cambridge, MA: MIT Press.
25.
Skovdin T. ( 2000) The Intergovernmental Panel on Climate Change. In: Andresen S, Skovdin T, Underdal A and Wettestad J (eds) Science and Politics in International Environmental Regimes. Manchester: University of Manchester Press, 146-180.
26.
Stewart D. ( 2005) Social status in an open-source community. American Sociological Review70: 823-842.
27.
Sundberg M. ( 2006) Credulous modellers and suspicious experimentalists? Comparison of model output and data in meteorological simulation modeling. Science Studies19(1): 52-68.
28.
Sundberg M.(2009) The everyday world of simulation modeling: The development of parameterizations in meteorology. Science, Technology, & Human Values34(2): 162-181.
Turkle S. ( 2004[1984]) The Second Self: Computers and the Human Spirit, 20th anniversary edn. Cambridge, MA: MIT Press.
31.
Van der Sluijs J., van Eijndhoven J., Shackley S. and Wynne B. ( 1998) Anchoring devices in science for policy: The case of consensus around climate sensitivity. Social Studies of Science28(2): 291-323.
32.
Weber M. ( 1978) Economy and Society: An Outline of Interpretive Sociology . Berkeley and Los Angeles: University of California Press.
