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Record W114458342 · doi:10.1177/070674371005500310

Measure for Measure: New Developments in Measurement and Item Response Theory

2010· article· en· W114458342 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueThe Canadian Journal of Psychiatry · 2010
Typearticle
Languageen
FieldDecision Sciences
TopicPsychometric Methodologies and Testing
Canadian institutionsBaycrest HospitalUniversity of Toronto
Fundersnot available
KeywordsItem response theoryClassical test theoryNormativeMeasure (data warehouse)PsychologyEconometricsPsychometricsTest (biology)Scale (ratio)Interpretation (philosophy)Cognitive psychologySample (material)Level of measurementStatisticsComputer scienceMathematicsClinical psychologyEpistemologyData mining

Abstract

fetched live from OpenAlex

For the past 70 years, test development has been dominated by what is called classical test theory (CTT). However, there are many problems associated with CTT, including: the resulting scales tend to be long; their interpretation is highly dependent on the normative sample; the assumption that each item contributes equally to the total score is often wrong, as is calculating a single index of measurement error for all possible scores; and it is difficult to equate different tests developed using CTT. Recently, a new approach to scale development has appeared, called item response theory (IRT), which overcomes all of these problems and, in certain cases, results in a scale with true interval-level properties. This article is an introduction to IRT. It concludes by discussing why IRT hasn't been adopted more widely, and some of its limitations.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.068
metaresearch head score (Gemma)0.137
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.324
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0680.137
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.334
GPT teacher head0.396
Teacher spread0.062 · 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