Empirical likelihood inference under stratified random sampling using auxiliary population information
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
Journal Article Empirical likelihood inference under stratified random sampling using auxiliary population information Get access Bob Zhong, Bob Zhong Department 07B2, Building AP5N, Abbott Laboratories, Abbott Park, Illinois 60064‐6070, U.S.A. bob.zhong@add.ssw.abbott.com School of Mathematics and Statistics, Carleton University, Ottawa, Ontario, K1S 5B6, Canada jrao@math.carleton.ca Search for other works by this author on: Oxford Academic Google Scholar J. N. K. Rao J. N. K. Rao Department 07B2, Building AP5N, Abbott Laboratories, Abbott Park, Illinois 60064‐6070, U.S.A. bob.zhong@add.ssw.abbott.com School of Mathematics and Statistics, Carleton University, Ottawa, Ontario, K1S 5B6, Canada jrao@math.carleton.ca Search for other works by this author on: Oxford Academic Google Scholar Biometrika, Volume 87, Issue 4, December 2000, Pages 929–938, https://doi.org/10.1093/biomet/87.4.929 Published: 01 December 2000
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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