NUMBERS
Volume 68 No. 2 (Summer 2001)
Arien Mack, Editor
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Paper summaries from the issue on
NUMBERS Volume 68 No. 2 (Summer 2001) Arien Mack, Editor |
Table of Contents
Paper summaries
In this essay, I examine the genealogy of the numeral transformation of emotions from its earliest beginnings in the late nineteenth century. My main thesis is that the historical encounter between emotion and number should not be viewed solely as a particular instantiation of more general trends in the development of objectifying, quantifying, or trust-building technologies. Rather, emotion-as-number provided an alternative medium for the circulation and expression of emotions in a culture that emphasized restraint. It also empowered the experimenter to produce forbidden emotions inside the modern laboratory; it participated in the construction of a uniquely scientific - in contradistinction to a poetic or feminized - emotion; and it attenuated the tensions that arose when "sublime" emotion was animalized in a Darwinian universe. In making this argument, I wish, among other things, to challenge recent claims concerning the repression of emotion in modern public culture. Emotion, I argue, was not restraint in post-Victorian culture, but rather communicated through a new medium - the number.
The practice of economic science is dominated by model building. To evaluate economic policy, models are built and used to produce numbers to inform us about economic phenomena. Although phenomena are detected through the use of observed data, they are in general not directly observable. To 'see' them we need instruments. More particularly, to obtain numerical facts of the phenomena we need measuring instruments. This paper will argue that in economics models function as such instruments of observation, more specific as measuring instruments. In measurement theory, measurement is a mapping of some class of aspects of characteristics of the empirical world into a set of numbers. The paper's view is that economic modelling is a specific kind of mapping to which the standard account on how models are obtained and assessed does not apply. Models are not easily or simply derived from theories and subsequently tested against empirical data. Instruments are constructed by integrating several theoretical and empirical ideas and requirements in such a way that their performance meets a beforehand chosen standard. The empirical requirement is that the model should take account of the phenomenological facts, so that the reliability of the model is not assessed by post-model testing but obtained by calibration.
It will be argued that the practice in which models are treated as measuring instruments is typical for 20th century economics and is most evident in the birth of two new branches, econometrics and macroeconomics during that century. It is here, particularly where both branches interact and overlap, that the discussion of the construction of reliable instruments emerges most clearly and where the solutions to this problem were worked out in practical and intellectual responses. These new practices can be associated with Nobel laureates like Ragnar Frisch and Jan Tinbergen, Tjalling Koopmans, Milton Friedman, Herbert Simon, Lawrence Klein, Trygve Haavelmo, and Robert Lucas who in their work treated these problems explicitly. Their contributions to macroeconomics and econometrics will be discussed in this paper focussing on attempts of modelling business-cycle phenomena.
Measurement is any process by which a value is assigned to the level or state of some quality of an object of study. This value is given numerical form, and measurement therefore involves the expression of information in quantities rather than by verbal statement. It provides a powerful means of reducing qualitative data to more condensed form for summarization, manipulation and analysis. The classical distinctions made by S S S Stevens between nominal, ordinal, interval and ratio measurement are a common starting point for discussion of social science measurement and the use of statistical techniques. This article considers the applicability of this model to research in the social sciences, particularly sociology and political science. Differences between classical measurement theory and practice in the social sciences are discussed. Problems in social science conceptualization, operationalization and measurement are examined, and a number of issues raised. Three cases are given particular attention, the Social Indicators movement, the variable of social class, and measures of race and ethnicity. The article concludes that the 'ineluctable fuzziness' of many social science concepts is inescapable, and that social measurement remains a difficult and challenging issue.
