Proceedings of the Survey Methods Section REPAIR OF TWO-PHASE CALIBRATION METHODOLOGY IN SURVEY SAMPLING
Bibliographic record
Abstract
In the present investigation, a question has been answered raised by the Deville and Särndal (1992) calibration approach to the eminent survey statisticians working at the U.S. Bureau of Census, Statistics Canada, Australian Bureau of Statistics, and their consultants from different universities across the world. This particular paper has been designed especially to repair the defective two-phase calibration methodology of Hidiroglou and Särndal (1995, 1998), and hence that of Singh (2000). The proposed methodology is based on the Golden Jubilee Year–2003, celebrated by Singh (2003, 2004a, 2004b), of the linear regression estimator owed to Hansen, Hurwitz and Madow (1953) for its outstanding performance. In this paper, it has been shown that chain regression estimator is unique among its class of estimators. There is no need of simulation study, because the repaired theoretical results are crystal clear. Based on Singh (2003, 2004a, 2004b) work, some modifications in the statistical package General Estimation System (GES), developed by the Statistics Canada, have been strongly recommended.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.203 | 0.309 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.023 | 0.007 |
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 itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".