Proceedings of the Survey Methods Section CHALLENGES SURROUNDING THE USE OF TAX DATA IN THE DEVELOPMENT OF QUARTERLY INDICATORS FOR VARIOUS SERVICE INDUSTRIES
Bibliographic record
Abstract
In the spring of 2005, Statistics Canada started the development of a new project called the “Quarterly Services Indicators”. This project aims at tracking the economic productivity on a quarterly basis for more than forty service industries. Administrative data, namely Goods and Services Tax (GST) data, constitute the foundation of this project in the sense that they directly supply the variable of interest, and are used for the vast majority of the units in the population. The use of GST data is complemented by a small survey of complex structured businesses in order to provide an accurate industrial and provincial profile of the revenue in the country. This paper gives a brief description of the project as well as of the GST data used. It also provides an overview of the methodological challenges encountered during the development stage of this project.
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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 | 0.657 | 0.591 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".