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
The Best Linear Unbiased (BLU) estimator (or predictor) of a population total is based on the following two assumptions: i) the estimation model underlying the BLU estimator is correctly specified and ii) the sampling design is ignorable with respect to the estimation model. In this context, an estimator is robust if it stays close to the BLU estimator when both assumptions hold and if it keeps good properties when one or both assumptions are not fully satisfied. Robustness with respect to deviations from assumption (i) is called model robustness while robustness with respect to deviations from assumption (ii) is called design robustness. The Generalized Regression (GREG) estimator is often viewed as being robust since its property of being Asymptotically Design Unbiased (ADU) is not dependent on assumptions (i) and (ii). However, if both assumptions hold, the GREG estimator may be far less efficient than the BLU estimator and, in that sense, it is not robust. The relative inefficiency of the GREG estimator as compared to the BLU estimator is caused by widely dispersed design weights. To obtain a design-robust estimator, we thus propose a compromise between the GREG and the BLU estimators. This compromise also provides some protection against deviations from assumption (i). However, it does not offer any protection against outliers, which can be viewed as a consequence of a model misspecification. To deal with outliers, we use the weighted generalized M-estimation technique to reduce the influence of units with large weighted population residuals. We propose two practical ways of implementing M-estimators for multipurpose surveys; either the weights of influential units are modified and a calibration approach is used to obtain a single set of robust estimation weights or the values of influential units are modified. Some properties of the proposed approach are evaluated in a simulation study using a skewed finite population created from real survey data. 1. Jean-Francois Beaumont and Asma Alavi, Household Survey Methods Division, Statistics Canada, 16 floor, R.H. Coats Building, Ottawa, Ontario, Canada, K1A 0T6. E-mail: Jean-Francois.Beaumont@statcan.ca and Asma.Alavi@statcan.ca.
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 | 0.000 | 0.001 |
| 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