SAS Global Forum 2007 Posters Paper 146-2007 Benchmarking Sub-Annual Series to Annual Totals – From Concepts to SAS
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
Situations that require benchmarking are very common in statistical agencies. Benchmarking is defined as an adjustment of the level of a sub-annual series using auxiliary annual benchmarks. The sub-annual series is modified so that the annual sums of the sub-annual series are equal to the corresponding benchmarks. This is done while preserving the movement in the sub-annual series as much as possible as well as considering that the benchmarks at the end of the series might not be available yet. This paper illustrates the benchmarking methodology developed at Statistics Canada. The presented method is a special case of the general regression-based benchmarking model proposed by Dagum and Cholette (2006). The paper also presents the innovative implementation of the methodology with a complete SAS procedure called PROC BENCHMARKING, developed at Statistics Canada for UNIX and Microsoft Windows operating systems, using SAS/TOOLKIT®. The procedure is presented through a custom add-in task for SAS Enterprise Guide and Microsoft Office, developed to provide a user-friendly interface and produce analytical tables and graphs based on the benchmarking results. The methodology presented is primarily used by economists and analysts, while the accompanying SAS procedure and custom task require basic knowledge of SAS and SAS Enterprise Guide.
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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 | 0.000 | 0.000 |
| 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) | 0.002 | 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