Construct-focused Configural Invariance for Measures Showing a Multi-dimensional Structure and Application to Exchange Test Data
Why this work is in the frame
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Bibliographic record
Abstract
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.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it