The Variability of Pseudo R2s in Logistic Regression Models
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
Over the past few decades, the use of logistic regression has increased in social and medical sciences research involving binary response variables. With respect to logistic regression, there is at present no widely accepted measure of explained variation with which one could judge the fit of a given model. A number of pseudo R2s have been proposed for the purpose. A number of studies carried out to compare and contrast their strengths, weaknesses and applicability indicate that these pseudo R2s vary considerably in terms of interpretability and range. This paper brings out the propensity of the various pseudo R2s to have different absolute values, different percentages of change from one model to another, and in some cases even vary in terms of their direction of change (i.e., increase versus decrease). This paper contributes to the literature by highlighting the variability of pseudo R2 and the importance of knowing which pseudo R2 is being utilized and its particular characteristics.
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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 | 0.006 | 0.002 |
| 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 | 0.001 |
| 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