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Record W2516573604 · doi:10.5539/ijsp.v5n5p55

Construct-focused Configural Invariance for Measures Showing a Multi-dimensional Structure and Application to Exchange Test Data

2016· article· en· W2516573604 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInternational Journal of Statistics and Probability · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSensory Analysis and Statistical Methods
Canadian institutionsnot available
Fundersnot available
KeywordsConstruct (python library)Measurement invarianceMetric (unit)Variance (accounting)MathematicsTest (biology)PopulationComponent (thermodynamics)StatisticsEconometricsConfirmatory factor analysisComputer scienceStructural equation modeling

Abstract

fetched live from OpenAlex

The concept of construct-focused configural invariance is proposed for investigating the measurement invariance of measures that need to be represented by means of a multi-dimensional model with constrained discriminability. The major characteristic of this concept is the concentration of the investigation of invariance on the component representing the process or processes associated with the construct of interest. This concept enables the exclusion of other components accounting for irrelevant but systematic variance from an investigation. Three large sets of Exchange Test data that differed according to the applied version of the Exchange Test and the population from which the samples originated were investigated according to this concept. Two samples were university students and the third one internet users. It was possible to establish construct-focused configural invariance and corresponding metric invariance for Exchange Test.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.866
Threshold uncertainty score0.335

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.089
GPT teacher head0.327
Teacher spread0.238 · 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