Achieving Data Quality in a Statistical Agency: A Methodological Perspective THE UNIFIED ENTERPRISE SURVEY ITS APPROACH TO QUALITY
Bibliographic record
Abstract
This paper discusses the approach that Statistics Canada has taken to improve the quality of annual business surveys through their integration in the Unified Enterprise Survey (UES). The primary objective of the UES is to measure the final annual sales of goods and services accurately by province, in sufficient detail and in a timely manner. This paper describes the methodological approaches that the UES has used to improve financial and commodity data quality in four broad areas. They are: improved coherence of the data collected from different levels of the enterprise; better coverage of industries; better depth of information, in the sense of more content detail and estimates for more detailed domains; and better consistency of the concepts and methods across industries. The approach, in achieving quality, has been (a) to establish a base measure of the quality of the business survey program before the UES; (b) to measure the annual data quality of the UES; and (c) to do specific studies to better understand the quality of UES data and methods.
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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 | 0.589 | 0.651 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.018 | 0.025 |
| Science and technology studies | 0.008 | 0.033 |
| Scholarly communication | 0.022 | 0.009 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".