246
are equal) together with measurement uncertainty. Calibration increases
measurement- related semantic information, and in fact makes measurement recognizable as such.
This semiotic perspective highlights the semantic role of measurement, but at the
same time encompasses the pragmatic scenario in which measurement acquires a
key role of enabler of data-driven decision-making processes (Mari & Petri, 2017)
through the comparison of measurement uncertainty with target uncertainty, the
condition—as discussed in Sect. 7.4.4—for considering measurement results actually useful to support the decision for which the measurement itself has been
designed and performed. In this broader context the evaluation of the quality of
measurement and its results becomes a complex subject, in which, together with
measurement uncertainty, several other conditions need to be taken into account,
such as the timeliness of the acquired information and its pertinence to the decision
to be made (Mari, Carbone, & Petri, 2012). The pragmatic import of measurement
is thus well summarized (Muller, 2018: p. 3):
“There are things that can be measured. There are things that are worth measuring. But what
can be measured is not always what is worth measuring; what gets measured may have no
relationship to what we really want to know. The costs of measuring may be greater than the
benefits. The things that get measured may draw effort away from the things we really care
about”.
Hence, measurement allows us to climb the semiotic layers, where, as depicted
in Fig. 8.1, the sequence
data syntactic information
semantic information useful pr
o
o
a agmatic
information
is then
indication values measurement results measurement results
o
o
i in a decision
making context
Let us now review the main stages that have led us here and that, in the final section,
will allow us to discuss the core question of this book: Can there be one meaning of
“measurement” across the sciences?
8.2 The path we have walked so far
In this book we have sought a characterization of capable of
explaining the acknowledged epistemic authority of measurement, but not needlessly tied to a specific subject matter or algebraic constraints.
Our starting point, in Chap. 2, was the identification of a basic set of necessary
conditions for measurement, hypothesized to be plausibly acceptable by most, if not
all, researchers and practitioners. The outcome of that chapter was the statement that
8 Conclusion
are equal) together with measurement uncertainty. Calibration increases
measurement- related semantic information, and in fact makes measurement recognizable as such.
This semiotic perspective highlights the semantic role of measurement, but at the
same time encompasses the pragmatic scenario in which measurement acquires a
key role of enabler of data-driven decision-making processes (Mari & Petri, 2017)
through the comparison of measurement uncertainty with target uncertainty, the
condition—as discussed in Sect. 7.4.4—for considering measurement results actually useful to support the decision for which the measurement itself has been
designed and performed. In this broader context the evaluation of the quality of
measurement and its results becomes a complex subject, in which, together with
measurement uncertainty, several other conditions need to be taken into account,
such as the timeliness of the acquired information and its pertinence to the decision
to be made (Mari, Carbone, & Petri, 2012). The pragmatic import of measurement
is thus well summarized (Muller, 2018: p. 3):
“There are things that can be measured. There are things that are worth measuring. But what
can be measured is not always what is worth measuring; what gets measured may have no
relationship to what we really want to know. The costs of measuring may be greater than the
benefits. The things that get measured may draw effort away from the things we really care
about”.
Hence, measurement allows us to climb the semiotic layers, where, as depicted
in Fig. 8.1, the sequence
data syntactic information
semantic information useful pr
o
o
a agmatic
information
is then
indication values measurement results measurement results
o
o
i in a decision
making context
Let us now review the main stages that have led us here and that, in the final section,
will allow us to discuss the core question of this book: Can there be one meaning of
“measurement” across the sciences?
8.2 The path we have walked so far
In this book we have sought a characterization of
explaining the acknowledged epistemic authority of measurement, but not needlessly tied to a specific subject matter or algebraic constraints.
Our starting point, in Chap. 2, was the identification of a basic set of necessary
conditions for measurement, hypothesized to be plausibly acceptable by most, if not
all, researchers and practitioners. The outcome of that chapter was the statement that
8 Conclusion