The DOI matches its Crossref record; the references it cites are not checked yet.
Measurement Fundamentals: A Pragmatic View
Abstract
Measurements are more and more required in an increasing variety of human activities to acquire reliable information useful for effectively supporting decision-making processes. Furthermore, the entities whose properties are to be measured and the measuring systems are becoming increasingly complex, hardly to be modeled and managed. In this rapidly evolving scenario, several issues concerning the fundamentals of measurement science and technology arise. The purpose of this paper is to discuss some aspects of the crucial question about which evaluation processes can be considered measurements. Rather than focusing on formal conditions or technological constraints, we propose a pragmatic characterization of measurement, under the assumption that a better comprehension of the concept can be achieved by identifying and discussing the basic features which justify the reliability attributed to measurement results. With such an interdisciplinary approach, this work aims at promoting a broad discussion among interested researchers, even working in different scientific disciplines, so to increase the synergies among different research areas and to improve the body of knowledge about measurement fundamentals.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Error methods are well established in laboratory medicine and are likely to be used for singleton internal quality control procedures and for proficiency testing for the foreseeable future and for quality assurance purposes because of their familiarity and proven value.15,24”
From Uncertainty in Measurement and Total Error (2017) · cites this paper as [24]
Citations
The citation lists haven’t been fetched yet
References 18 · Cited by 64
Research works printed but not linked: 11
- 2005, Quality Management SystemsFundamentals and Vocabulary
- popper, 1994, Knowledge and the BodyMind ProblemIn Defense of Interaction
- 2006, StatisticsVocabulary and SymbolsPart 1 General Statistical Terms and Terms Used in Probability
- 2008, International Vocabulary of MetrologyBasic and General Concepts and Associated Terms
- 2008, Evaluation of measurement dataGuide to the expression of uncertainty in measurement
- krantz, 1971, Foundations of Measurement, 1
- 0, Evolving Needs for Metrology in Trade Industry and Society and the Role of the BIPM
- 1998, Accuracy (Trueness and Precision) of Measurement Methods and ResultsPart 1 General Principles and Definitions
- 2001, Software EngineeringProduct QualityPart 1 Quality Model
- 2011, Assessment Framework and Guidance on Submissions
- 0, The EFQM Excellence Model
Metadata
- Authors
- Mari, L., Carbone, P., & Petri, D.
- Affiliations
- University Carlo Cattaneo (IT)
- University of Perugia (IT)
- University of Trento (IT)
- Venue
- IEEE Transactions on Instrumentation and Measurement
- Published
- 2012-08
- Volume
- 61
- Issue
- 8
- Pages
- 2107-2115
- ISSN
- 0018-9456 (ISSN-L)
- 1557-9662
- OpenAlex
- W2011797001
- Cited by
- 64
- References
- 18
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 2 · 2023: 9 · 2022: 3 · 2021: 10 · 2020: 3 · 2019: 2 · 2018: 3 · 2017: 9 · 2016: 3 · 2015: 8 · 2014: 2 · 2013: 3
- FWCI
- 5.09
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Instrumentation and Measurement (publishedVersion)
- Institutional Research Information System (Università degli Studi di Trento) (submittedVersion)
- Topics
- Physical Sciences › Physics and Astronomy › Instrumentation › Scientific Measurement and Uncertainty Evaluation
- Social Sciences › Decision Sciences › Management Science and Operations Research › Psychometric Methodologies and Testing
- Physical Sciences › Physics and Astronomy › Instrumentation › Sensor Technology and Measurement Systems
- Keywords
- measurement fundamentals, metrology, measurement technology, decision support
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Measurement Fundamentals: A Pragmatic View. https://app.cochat.ai/papers/10.1109/tim.2012.2193693
- The paper
- Mari, L., Carbone, P., & Petri, D. (2012). Measurement Fundamentals: A Pragmatic View. IEEE Transactions on Instrumentation and Measurement. https://doi.org/10.1109/tim.2012.2193693