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Record W2013184334 · doi:10.1002/smr.496

Measurement and quantification are not the same: ISO 15939 and ISO 9126

2010· article· en· W2013184334 on OpenAlexaff
Alain Abran, Jean‐Marc Desharnais, Juan J. Cuadrado‐Gallego

Bibliographic record

VenueJournal of Software Evolution and Process · 2010
Typearticle
Languageen
FieldDecision Sciences
TopicScientific Measurement and Uncertainty Evaluation
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsMetrologyMeasure (data warehouse)Set (abstract data type)Field (mathematics)Level of measurementComputer scienceUnits of measurementMeasurement uncertaintyScale (ratio)Data miningIndustrial engineeringReliability engineeringSystems engineeringOperations researchMathematicsEngineeringStatisticsGeographyProgramming languagePhysics

Abstract

fetched live from OpenAlex

SUMMARY Measurement based on the international standards for measurement (i.e., metrology) is not the same as judgmental‐based quantification of implicit relationships across a mix of entities and attributes without due consideration of admissible mathematical operations on numbers of different scale types. This paper presents first the Measurement Information Model in ISO 15939 and clarifies next what in it refers to the classical metrology field, and what refers to the quantitative analysis of relationships. The paper concludes with two examples of the designs of a measure for ISO 9126, one focusing on a single attribute and the second attempting to quantify a set of relationships across a number of entities and attributes. Copyright © 2010 John Wiley & Sons, Ltd.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.030
metaresearch head score (Gemma)0.046
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.030
Threshold uncertainty score0.161

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0300.046
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.005
Science and technology studies0.0010.010
Scholarly communication0.0100.011
Open science0.0020.005
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.178
GPT teacher head0.382
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations11
Published2010
Admission routes1
Has abstractyes

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