235
• Users are generally interested in the information, not the way it is produced;
from the user’s point of view, objectivity and intersubjectivity are features of the
products of the process, i.e., of measurement results.
• Designers are interested in the way the information may be produced; from the
designer’s point of view, objectivity and intersubjectivity are features of the process, then inherited by its products, i.e., first of all of measurement.
Hence, we can see objectivity and intersubjectivity as features of both the process
(i.e., measurement) and the products (i.e., measurement results).
27
As they have been characterized, objectivity and intersubjectivity are embedded
in measuring systems: in other words, measuring systems are designed, set up
(including via their calibration), and operated so as to be able to produce information with the expected degree of objectivity and intersubjectivity, i.e., able to produce measurement results with a measurement uncertainty which is less than target
uncertainty, the “upper limit [of uncertainty] decided on the basis of the intended
use of measurement results” (JCGM, 2012: 2.34). This highlights the pragmatic
nature of measurement: what counts as high or low quality is relative to the purpose
of the measurement; if a comparatively lower quality instrument provides results of
sufficient accuracy, using it could lead to still acceptable (and cheaper)
measurements.
7.4.4 Can measurement be “bad”?
According to the characterization we have just proposed, objectivity and intersubjectivity are independent features: something can be objective but not intersubjective (as might happen in the case of the usage of an uncalibrated measuring system),
or vice versa (as when the result of an evaluation is expressed in the customary
format for the values of quantities, i.e., number times unit, but is obtained through a
badly flawed measurement). Together they identify the two dimensions of quality of
measurement: the claim of the possibilities of obtaining information about empirical properties, and of socially reporting such information.
28
It is thus through their
objectivity and intersubjectivity that measurement results are considered to be of
weak definition. It implies nothing about truth to nature. It has more to do with the exclusion of
judgment, the struggle against subjectivity” (Porter, 1995: p. ix).
27 As noted above, the concept of validity in human science measurement is closely related to definitional uncertainty. However, it is an expansive concept, and hence, there are aspects of it that also
overlap with other parts of objectivity and intersubjectivity.
28 Interestingly, these dimensions correspond to the two main stages of a measurement process of
(a) transduction and matching and (b) calibration mapping, as connected by the local scale application. The idea that measurement is to be modeled on the basis of such two stages is not unusual,
though the terms may be different. For example, Roman Morawski calls them “conversion” and
“reconstruction” (Morawski, 2013) and Giovanni Battista Rossi and Francesco Crenna call them
“observation” and “restitution” (Rossi & Crenna, 2018).
7.4 Measurement quality according to the model
• Users are generally interested in the information, not the way it is produced;
from the user’s point of view, objectivity and intersubjectivity are features of the
products of the process, i.e., of measurement results.
• Designers are interested in the way the information may be produced; from the
designer’s point of view, objectivity and intersubjectivity are features of the process, then inherited by its products, i.e., first of all of measurement.
Hence, we can see objectivity and intersubjectivity as features of both the process
(i.e., measurement) and the products (i.e., measurement results).
27
As they have been characterized, objectivity and intersubjectivity are embedded
in measuring systems: in other words, measuring systems are designed, set up
(including via their calibration), and operated so as to be able to produce information with the expected degree of objectivity and intersubjectivity, i.e., able to produce measurement results with a measurement uncertainty which is less than target
uncertainty, the “upper limit [of uncertainty] decided on the basis of the intended
use of measurement results” (JCGM, 2012: 2.34). This highlights the pragmatic
nature of measurement: what counts as high or low quality is relative to the purpose
of the measurement; if a comparatively lower quality instrument provides results of
sufficient accuracy, using it could lead to still acceptable (and cheaper)
measurements.
7.4.4 Can measurement be “bad”?
According to the characterization we have just proposed, objectivity and intersubjectivity are independent features: something can be objective but not intersubjective (as might happen in the case of the usage of an uncalibrated measuring system),
or vice versa (as when the result of an evaluation is expressed in the customary
format for the values of quantities, i.e., number times unit, but is obtained through a
badly flawed measurement). Together they identify the two dimensions of quality of
measurement: the claim of the possibilities of obtaining information about empirical properties, and of socially reporting such information.
28
It is thus through their
objectivity and intersubjectivity that measurement results are considered to be of
weak definition. It implies nothing about truth to nature. It has more to do with the exclusion of
judgment, the struggle against subjectivity” (Porter, 1995: p. ix).
27 As noted above, the concept of validity in human science measurement is closely related to definitional uncertainty. However, it is an expansive concept, and hence, there are aspects of it that also
overlap with other parts of objectivity and intersubjectivity.
28 Interestingly, these dimensions correspond to the two main stages of a measurement process of
(a) transduction and matching and (b) calibration mapping, as connected by the local scale application. The idea that measurement is to be modeled on the basis of such two stages is not unusual,
though the terms may be different. For example, Roman Morawski calls them “conversion” and
“reconstruction” (Morawski, 2013) and Giovanni Battista Rossi and Francesco Crenna call them
“observation” and “restitution” (Rossi & Crenna, 2018).
7.4 Measurement quality according to the model