Integrating Alternative and Administrative Data into the Monthly Business Statistics: Some Applications from Statistics <scp>C</scp> anada
Bibliographic record
Abstract
In recent years, Statistics Canada has expanded the use of administrative data in many of its programs in the context of its modernization initiatives. Unsurprisingly, the monthly business statistics programs were no exception with some of them starting to integrate alternative data and/or expand the use of administrative data. These new approaches aimed to reduce the response burden, provide more granular data, and/or reduce the collection cost while improving or maintaining quality. This chapter will present applications in the use of alternative or administrative data in three monthly business programs. The first one is the replacement of survey data with the Goods and Services Tax and the Harmonized Sales Tax (GST) data in the Monthly Survey of Food Services and Drinking Places. The second one is the use of scanner data to replace survey commodity data in the Retail Commodity Survey. The last one is the use of small area estimation techniques that integrate survey data and administrative data in the Monthly Survey of Manufacturing. For each project, a description of the context, the methodology, and the impact will be described. Lastly, the impact of the pandemic and future initiatives for the monthly business statistics programs will be discussed briefly.
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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 | 0.017 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.026 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".