Reducing the sensitivity to nuisance parameters in pseudo‐likelihood functions
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 In a parametric model, parameters are often partitioned into parameters of interest and nuisance parameters. However, as the data structure becomes more complex, inference based on the full likelihood may be computationally intractable or sensitive to potential model misspecification. Alternative likelihood‐based methods proposed in these settings include pseudo‐likelihood and composite likelihood. We propose a simple adjustment to these likelihood functions to reduce the impact of nuisance parameters. The advantages of the modification are illustrated through examples and reinforced through simulations. The adjustment is still novel even if attention is restricted to the profile likelihood. The Canadian Journal of Statistics 42: 544–562; 2014 © 2014 Statistical Society of Canada
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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 | 0.009 |
| 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