256
that provides the required epistemic justification,
12
through the components of the
Framework whose behavior explains how a Basic Evaluation Equation is obtained.
In other words, the condition of epistemic justification that characterizes measurement is embedded in the Framework, and therefore is inherited by any process that
is structured accordingly.
The evaluation of uncertainty in measurement has an important role in this, as
clearly presented in the opening section of the Guide to the expression of uncertainty in measurement (GUM): “When reporting the result of a measurement of a
property, it is obligatory that some quantitative indication of the quality of the result
be given so that those who use it can assess its trustworthiness. Without such an
indication, measurement results cannot be compared, either among themselves or
with reference values given in a specification or standard” (JCGM, 2008: 0.1;
adapted, having substituted “physical quantity” with “property” and “reliability”, a
term with several other technical meanings, with “trustworthiness”). In any nontrivial measurement there are indeed multiple sources of uncertainty, and collecting
and combining them in an uncertainty budget (JCGM, 2012: def. 2.33) require that
the box is opened and the features of what is inside, i.e., the models of the object
under measurement and of the measurement, are explicated. The position of considering measurement to be a process that produces explicitly justifiable information is
then coherent with the quoted principle of the GUM:
(i) Any measurement result conveys a given quantity of information on the measurand, such that the greater the conveyed quantity of information the higher
the assumed quality of the result (the GUM refers to this in terms of “quantitative indication of the quality of the result”).
(ii) The justification to be provided relates in particular to the quality of the result,
and therefore to the quantity of information conveyed through it.
(iii) The quality of the result can be specified in terms of measurement uncertainty,
such that the greater the quality the less the uncertainty.
From the point of view of the user of a measurement result, the information about
the measurement uncertainty in the result has then the critical role of being an effective substitute for actually opening the box, of course under the condition that the
uncertainty is evaluated and reported in an honest way. The GUM offers a very clear
proviso about the fact that “the evaluation of uncertainty is neither a routine task nor
a purely mathematical one; it depends on detailed knowledge of the nature of the
measurand and of the measurement. The quality and utility of the uncertainty quoted
for the result of a measurement therefore ultimately depend on the understanding,
critical analysis, and integrity of those who contribute to the assignment of its value”
12 We are referring here to the epistemic justification of measurement results, and therefore to the
principled possibility of interpreting the information produced by measurement in a social context
where it becomes shared knowledge. Higher level forms of justification are not only possible but
usually also desirable for measurement, and in particular pragmatic justification, aimed at showing
that the measurement results deserve the resources used for obtaining them.
8 Conclusion
that provides the required epistemic justification,
12
through the components of the
Framework whose behavior explains how a Basic Evaluation Equation is obtained.
In other words, the condition of epistemic justification that characterizes measurement is embedded in the Framework, and therefore is inherited by any process that
is structured accordingly.
The evaluation of uncertainty in measurement has an important role in this, as
clearly presented in the opening section of the Guide to the expression of uncertainty in measurement (GUM): “When reporting the result of a measurement of a
property, it is obligatory that some quantitative indication of the quality of the result
be given so that those who use it can assess its trustworthiness. Without such an
indication, measurement results cannot be compared, either among themselves or
with reference values given in a specification or standard” (JCGM, 2008: 0.1;
adapted, having substituted “physical quantity” with “property” and “reliability”, a
term with several other technical meanings, with “trustworthiness”). In any nontrivial measurement there are indeed multiple sources of uncertainty, and collecting
and combining them in an uncertainty budget (JCGM, 2012: def. 2.33) require that
the box is opened and the features of what is inside, i.e., the models of the object
under measurement and of the measurement, are explicated. The position of considering measurement to be a process that produces explicitly justifiable information is
then coherent with the quoted principle of the GUM:
(i) Any measurement result conveys a given quantity of information on the measurand, such that the greater the conveyed quantity of information the higher
the assumed quality of the result (the GUM refers to this in terms of “quantitative indication of the quality of the result”).
(ii) The justification to be provided relates in particular to the quality of the result,
and therefore to the quantity of information conveyed through it.
(iii) The quality of the result can be specified in terms of measurement uncertainty,
such that the greater the quality the less the uncertainty.
From the point of view of the user of a measurement result, the information about
the measurement uncertainty in the result has then the critical role of being an effective substitute for actually opening the box, of course under the condition that the
uncertainty is evaluated and reported in an honest way. The GUM offers a very clear
proviso about the fact that “the evaluation of uncertainty is neither a routine task nor
a purely mathematical one; it depends on detailed knowledge of the nature of the
measurand and of the measurement. The quality and utility of the uncertainty quoted
for the result of a measurement therefore ultimately depend on the understanding,
critical analysis, and integrity of those who contribute to the assignment of its value”
12 We are referring here to the epistemic justification of measurement results, and therefore to the
principled possibility of interpreting the information produced by measurement in a social context
where it becomes shared knowledge. Higher level forms of justification are not only possible but
usually also desirable for measurement, and in particular pragmatic justification, aimed at showing
that the measurement results deserve the resources used for obtaining them.
8 Conclusion