Monitoring Survey Processes of the Canadian Monthly Wholesale and Retail Trade Survey
Bibliographic record
Abstract
The Canadian Monthly Wholesale and Retail Trade Survey (MWRTS) produces monthly estimates for sales and inventories at various province and industry levels. The MWRTS has recently been redesigned, in part, to provide estimates for the new North American Industry Classification System (NAICS) and to take full advantage of administrative data from the Goods and Services Tax program. The redesign also addressed the need to maintain the quality of the estimates, to reduce cost and respondent burden, to update computer systems, and to harmonise concepts and methods with the annual survey. As part of the redesign, different tools were developed to ensure proper monitoring of survey steps. The first type of ‘diagnostic ’ tool aims to assess the functionality of the modules in each survey step while the second type involves monthly descriptive statistics, such as number of live units in the sample, imputation methods used and recurrent top contributors. These statistics are studied longitudinally to detect changes that affect the estimates. A third set of diagnostic tools is used for analysis of level and trend estimates. Finally, some of the major tools used on a monthly basis for this survey will be described in this paper.
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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 | 0.020 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.018 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".