Sampling and Estimation Issues for Annual and Sub‐annual Canadian Business Surveys
Bibliographic record
Abstract
Summary A typical Business Register (BR) is mainly based on administrative data files provided by organisations that produce them as a by‐product of their function. Such files do not necessarily yield a perfect Business Register. A good BR should have the following characteristics: (1) It should reflect the complex structures of businesses with multiple activities, in multiple locations or with multiple legal entities; (2) It should be free of duplication, extraneous or missing units; (3) It should be properly classified in terms of key stratification variables, including size, geography and industry; (4) It should be easily updateable to represent the “newer” business picture, and not lag too much behind it. In reality, not all these desirable features are fully satisfied, resulting in a universe that has missing units, inaccurate structures, as well as improper contact information, to name a few defects. These defects can be compensated by using sampling and estimation procedures. For example, coverage can be improved using multiple frame techniques, and the sample size can be increased to account for misclassification of units and deaths on the register. At the time of estimation, auxiliary information can be used in a variety of ways. It can be used to impute missing variables, to treat outliers, or to create synthetic variables obtained via modelling. Furthermore, time lags between the birth of units and the time that they are included on the register can be accounted for appropriately inflating the design‐based estimates.
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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 | 0.112 | 0.264 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".