Interpretable data reduction in prediction modeling: extended redundancy analysis and its extensions and applications
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.
Bibliographic record
Abstract
With technical advances in measuring human behaviors and psychological traits, research goals in psychology increasingly call for statistical methods for data with complex structure.Modeling such data is challenging because they typically contain a large number of redundant predictor dimensions and potentially heterogeneous subgroups of observations.Moreover, properly handling the covariance structure of repeated or clustered measures is critical when observations are correlated rather than independent.Assessment of model performance on independent unseen data is also important as it guides the choice of final model structure when researchers seek to develop models that can generalize beyond the current sample.The present research proposes solutions to these specific challenges in the framework of extended redundancy analysis (ERA).ERA is a statistical tool that performs data reduction Chapter 3. Regularized Extended Redundancy Analysis via
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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