An Examination of Parametric and Nonparametric Dimensionality Assessment Methods with Exploratory and Confirmatory Mode
Why this work is in the frame
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Bibliographic record
Abstract
The aim of the present research study was to compare the findings from the nonparametric MSA, DIMTEST and DETECT and the parametric dimensionality determining methods in various simulation conditions by utilizing exploratory and confirmatory methods. For this purpose, various simulation conditions were established based on number of dimensions, number of items, item discrimination levels, sample size and correlation between dimensions values. The performance of dimensionality determining methods based on MSA and factor analysis are similar, yet MSA is more effective in determining the number of dimensions. However, the method of DETECT has displayed a more powerful performance when compared with the other dimensionality methods. Particularly the confirmatory DETECT method could reveal the true dimensionality in conditions of both low discrimination and high discrimination methods. On the other hand, the exploratory DETECT method was affected by discrimination and, thus, could perform well only with high-discrimination items. In conditions where the exploratory dimensionality reduction methods are used to determine the number of dimensions, it is beneficial to confirm this structure by using confirmatory dimensionality reduction methods. For this purpose, using confirmatory DETECT is particularly recommended.
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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 | 0.000 |
| 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 | 0.001 |
| 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