194
lar, it is clear that measurement of nonphysical properties cannot conform to
expectations based on the traditional realization of measuring systems operating on
the basis of transduction effects implemented by physical sensors. As a consequence, interpretations of measurement have become so abstract that they may be
unable to provide a convincing and useful demarcation of measurement from formally similar processes that are generally thought to lack epistemic authority, such
as most statements of subjective judgments and opinions.
Our position on this matter is pragmatic: there is a social interest in sharing a
scientific and technical concept system across disciplines,
1
particularly in the case
of an infrastructural activity like measurement, and there is a social acknowledgment of the epistemic authority of measurement, which has critical consequences in
particular in terms of public trust attributed to the outcomes of putative measurement processes and the resources devoted to such processes. If the claim that a given
evaluation process is a measurement could be invoked at will, without understanding or concern for what has historically made it a valued practice, measurement
itself would become simply a rhetorical device, risking the discredit of its practice
in general.
In Sect. 4.4.2 we presented our claim that measurement is most appropriately
characterized by empirical rather than mathematical conditions, as grounded on a
model-dependent realism, introduced in Sect. 4.5 and developed in Chaps. 5 and 6,
about the objects of measurement, i.e., properties. We develop here that claim by
proposing that measurement is a process characterized by its structure, not only by
the specification of the relationship connecting its inputs to its outputs: what is
required for justifying the dependability of measurement results is an explanation of
how the process does what it does. Whereas an input-output relationship relies
solely on a black box model, a structural model involves identification of the invariant aspects of the empirical process, and therefore looks “inside the black box”. And
this, we argue, is what provides justification for the claim that measurement results
are publicly trustworthy. As a corollary, any purely black box model cannot adequately account for all the relevant features of measurement, and thus is not sufficient for the purpose of understanding the quality of measurement results.
As already highlighted, the conditions presented in Chap. 2—i.e., that measurement (i) is both an empirical and an informational process, (ii) designed on purpose,
(iii) whose input is an empirical property of an object, and that (iv) produces information in the form of values of that property—are necessary but not sufficient for a
process to be considered a measurement. We propose here that the missing sufficient conditions are provided by a structural model of the process of measuring. As
1 A basic reason for the complexity of this endeavor is the (usually unavoidable and in fact appropriate) specialization of the scientific and technical disciplines, which triggers the construction of
specific terminologies. An interesting example of an attempt to overcome lexical hyper-specialization while maintaining scientific and technical correctness is Electropedia, “the world’s most comprehensive online terminology database on ‘electrotechnology’, containing more than 22,000
terminological entries [...] organized by subject area” (Electropedia makes the series of standards
IEC 60050 freely accessible online at www.electropedia.org)
7 Modeling measurement and its quality
lar, it is clear that measurement of nonphysical properties cannot conform to
expectations based on the traditional realization of measuring systems operating on
the basis of transduction effects implemented by physical sensors. As a consequence, interpretations of measurement have become so abstract that they may be
unable to provide a convincing and useful demarcation of measurement from formally similar processes that are generally thought to lack epistemic authority, such
as most statements of subjective judgments and opinions.
Our position on this matter is pragmatic: there is a social interest in sharing a
scientific and technical concept system across disciplines,
1
particularly in the case
of an infrastructural activity like measurement, and there is a social acknowledgment of the epistemic authority of measurement, which has critical consequences in
particular in terms of public trust attributed to the outcomes of putative measurement processes and the resources devoted to such processes. If the claim that a given
evaluation process is a measurement could be invoked at will, without understanding or concern for what has historically made it a valued practice, measurement
itself would become simply a rhetorical device, risking the discredit of its practice
in general.
In Sect. 4.4.2 we presented our claim that measurement is most appropriately
characterized by empirical rather than mathematical conditions, as grounded on a
model-dependent realism, introduced in Sect. 4.5 and developed in Chaps. 5 and 6,
about the objects of measurement, i.e., properties. We develop here that claim by
proposing that measurement is a process characterized by its structure, not only by
the specification of the relationship connecting its inputs to its outputs: what is
required for justifying the dependability of measurement results is an explanation of
how the process does what it does. Whereas an input-output relationship relies
solely on a black box model, a structural model involves identification of the invariant aspects of the empirical process, and therefore looks “inside the black box”. And
this, we argue, is what provides justification for the claim that measurement results
are publicly trustworthy. As a corollary, any purely black box model cannot adequately account for all the relevant features of measurement, and thus is not sufficient for the purpose of understanding the quality of measurement results.
As already highlighted, the conditions presented in Chap. 2—i.e., that measurement (i) is both an empirical and an informational process, (ii) designed on purpose,
(iii) whose input is an empirical property of an object, and that (iv) produces information in the form of values of that property—are necessary but not sufficient for a
process to be considered a measurement. We propose here that the missing sufficient conditions are provided by a structural model of the process of measuring. As
1 A basic reason for the complexity of this endeavor is the (usually unavoidable and in fact appropriate) specialization of the scientific and technical disciplines, which triggers the construction of
specific terminologies. An interesting example of an attempt to overcome lexical hyper-specialization while maintaining scientific and technical correctness is Electropedia, “the world’s most comprehensive online terminology database on ‘electrotechnology’, containing more than 22,000
terminological entries [...] organized by subject area” (Electropedia makes the series of standards
IEC 60050 freely accessible online at www.electropedia.org)
7 Modeling measurement and its quality