Mediation analysis with latent factors using simultaneous group‐wise and parameter‐wise penalization
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
Mediation analysis aims to uncover the underlying mechanism of how an exposure variable affects the outcome of interest through one or more than one mediating variables. In the event that the number of candidate mediators is large, variable selection or dimension reduction techniques are often utilized to reduce the dimension of the initial set of mediators. In this paper, we propose a latent variable approach using sparse factor analysis with both group‐wise and parameter‐wise penalization to remove irrelevant candidate mediators and estimate the latent factors simultaneously. After the low‐dimensional latent mediating factors are obtained, the direct and indirect effects can be estimated and tested from a multivariate mediation model. To demonstrate the practical applications of the proposed methodology, we apply it to a weight behaviour dataset and an environmental dataset, separately.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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