Reweighted penalized regression for convenience samples
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
Abstract Modern epidemiological studies are often characterized by extensive data collection, which facilitates building high‐dimensional predictive models. With large samples often conveniently sampled, weighted penalized regression models are commonly applied to provide improved prediction. In this article, we empirically show that weighted ridge regression models may yield suboptimal results because of the lack of flexibility in the penalty structure. We propose a generalized weighted ridge regression (GWRR) estimation procedure that allows for the adjustment of sampling weights in the penalty structure. We derive the asymptotic properties of the proposed GWRR estimator and provide a computationally efficient closed‐form solution. We demonstrate the performance of the proposed GWRR estimator and justify the asymptotic variance via simulation studies. Finally, we illustrate the utility of our proposed estimator through an application to the prediction of mini‐mental state examination (MMSE) scores.
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 | 0.007 |
| 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