236
good quality. Since objectivity and intersubjectivity are not Boolean (i.e., yes–no)
conditions, in a given operational situation one could set a threshold of minimum
acceptable objectivity and intersubjectivity, aimed at guaranteeing that the results of
measurement will be useful for their intended use. This highlights the pragmatic
nature of measurement: the same measurement results might be considered good for
some purposes and bad for some others. Hence, objectivity and intersubjectivity are
features of good measurements, not of measurement as such. This allows to be an acceptable concept (i.e., not all measurements are good), where
“bad” is meant as
29
The objectivity and intersubjectivity of measurement results may be interpreted
as their overall “degree of quality”, which is (inversely) specified and quantified by
measurement uncertainty: a good measurement produces measurement results
whose uncertainty is
• beyond the definitional uncertainty of the measurand (again, a measurement
uncertainty less than definitional uncertainty corresponds to a waste of resources
devoted to design and perform the measurement), but
• less than the specified target uncertainty (a measurement uncertainty greater than
target uncertainty corresponds to a useless measurement).
An emphasis on sufficient objectivity and intersubjectivity for a given purpose is
then operationally useful, for the general guidelines it provides regarding the design
and performance of measurements (e.g., in Petri, Mari, & Carbone, 2015), but it is
still too specific at least in one respect: it would assume that measurement is always
good measurement. While pragmatically this is sound—if we know that what we
are doing is a bad measurement we (hopefully) avoid doing it—the concept of “bad
measurement” as such is not contradictory, and bad measurements do not fulfill the
condition of sufficient objectivity and intersubjectivity. In other words, in order to
maintain the VIM’s characterization of “reasonableness”, objectivity and intersubjectivity are useful but still not sufficient: some other conditions have to be identified. This, among other things, is discussed in the next chapter.
References
Baratto, A. C. (2008). Measurand: A cornerstone concept in metrology. Metrologia, 45, 299–307.
Bich, W. (2008). How to revise the GUM? Accreditation Quality and Assurance, 13, 271–255.
Boumans, M. (2007). Invariance and calibration. In M. Boumans (Ed.), Measurement in economics: A handbook (pp. 231–248). London: Academic Press.
Campbell, N. R. (1920). Physics—The elements. Cambridge: Cambridge University Press.
29 This implies that cannot be defined in terms of objectivity and intersubjectivity,
as instead sometimes suggested (e.g., “the result of measurement must meet the condition of objective truth”, Piotrowski, 1992: p. 1), also, mistakenly, by one of the present authors (Mari et al.,
2012: p. 2109).
7 Modeling measurement and its quality
good quality. Since objectivity and intersubjectivity are not Boolean (i.e., yes–no)
conditions, in a given operational situation one could set a threshold of minimum
acceptable objectivity and intersubjectivity, aimed at guaranteeing that the results of
measurement will be useful for their intended use. This highlights the pragmatic
nature of measurement: the same measurement results might be considered good for
some purposes and bad for some others. Hence, objectivity and intersubjectivity are
features of good measurements, not of measurement as such. This allows
“bad” is meant as
29
The objectivity and intersubjectivity of measurement results may be interpreted
as their overall “degree of quality”, which is (inversely) specified and quantified by
measurement uncertainty: a good measurement produces measurement results
whose uncertainty is
• beyond the definitional uncertainty of the measurand (again, a measurement
uncertainty less than definitional uncertainty corresponds to a waste of resources
devoted to design and perform the measurement), but
• less than the specified target uncertainty (a measurement uncertainty greater than
target uncertainty corresponds to a useless measurement).
An emphasis on sufficient objectivity and intersubjectivity for a given purpose is
then operationally useful, for the general guidelines it provides regarding the design
and performance of measurements (e.g., in Petri, Mari, & Carbone, 2015), but it is
still too specific at least in one respect: it would assume that measurement is always
good measurement. While pragmatically this is sound—if we know that what we
are doing is a bad measurement we (hopefully) avoid doing it—the concept of “bad
measurement” as such is not contradictory, and bad measurements do not fulfill the
condition of sufficient objectivity and intersubjectivity. In other words, in order to
maintain the VIM’s characterization of “reasonableness”, objectivity and intersubjectivity are useful but still not sufficient: some other conditions have to be identified. This, among other things, is discussed in the next chapter.
References
Baratto, A. C. (2008). Measurand: A cornerstone concept in metrology. Metrologia, 45, 299–307.
Bich, W. (2008). How to revise the GUM? Accreditation Quality and Assurance, 13, 271–255.
Boumans, M. (2007). Invariance and calibration. In M. Boumans (Ed.), Measurement in economics: A handbook (pp. 231–248). London: Academic Press.
Campbell, N. R. (1920). Physics—The elements. Cambridge: Cambridge University Press.
29 This implies that
as instead sometimes suggested (e.g., “the result of measurement must meet the condition of objective truth”, Piotrowski, 1992: p. 1), also, mistakenly, by one of the present authors (Mari et al.,
2012: p. 2109).
7 Modeling measurement and its quality