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What happens to facts after their construction?: characteristics and functional roles of facts in the dissemination of knowledge across modelling communities

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  • Erika Mansnerus
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    Abstract

    The core question addressed in this paper is: What happens to facts after their construction? The main contribution is to analyse the different practices of disseminating, circulating and crossfertilizing model-produced facts about Haemophilus influenzae type b and Streptococcus pneumoniae bacterial infections and the preventive public health measures against the invasive disease forms. Through the analysis, the paper shows how facts become characterised in different utilizing communities. It elaborates an account of the functional roles of facts that are capable of shaping the knowledge practices in the receiving communities. These analyses suggest how facts can travel beyond their production sites to be used as evidence in other domains.

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    File URL: http://eprints.lse.ac.uk/22504/
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    Bibliographic Info

    Paper provided by London School of Economics and Political Science, Department of Economic History in its series Economic History Working Papers with number 22504.

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    Length: 45 pages
    Date of creation: Jul 2008
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    Handle: RePEc:ehl:wpaper:22504

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    Postal: LSE, Dept. of Economic History Houghton Street London, WC2A 2AE, U.K.
    Phone: +44 (0) 20 7955 7084
    Web page: http://www.lse.ac.uk/economicHistory/
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    1. Cauchemez, Simon & Temime, Laura & Guillemot, Didier & Varon, Emmanuelle & Valleron, Alain-Jacques & Thomas, Guy & Boelle, Pierre-Yves, 2006. "Investigating Heterogeneity in Pneumococcal Transmission: A Bayesian MCMC Approach Applied to a Follow-up of Schools," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 946-958, September.
    2. Francesco Bartolucci, 2006. "Likelihood inference for a class of latent Markov models under linear hypotheses on the transition probabilities," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(2), pages 155-178.
    3. Mervi Eerola & Dario Gasbarra & P. Helena Mäkelä & Henri Linden & Andrei Andreev, 2003. "Joint Modelling of Recurrent Infections and Antibody Response by Bayesian Data Augmentation," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 30(4), pages 677-698.
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