Managing Response Burden by Controlling Sample Selection and Survey Coverage
Bibliographic record
Abstract
Statistical agencies are constantly making efforts to control the response burden of their household and business survey respondents. Statistics Canada’s Survey on Employment, Payroll and Hours is no exception. This monthly business survey, which produces estimates and determines the month-to-month changes for variables such as employment, earnings and hours at detailed industrial levels for Canada, the provinces and the territories, currently manages response burden by making use of administrative data and by having rules that prevent establishments from rotating in the sample too soon after being rotated out. Recently, two new ideas to decrease even more the response burden for respondents to this survey have been studied. The first is to control the overlap of the samples from one month to the next by the use of the microstrata method (Riviere (2001)) in the sample selection process. The second is the increased number of establishments in the take-none strata. This paper will present the studies that evaluated the pros and cons of implementing each of these new features in the survey.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.484 | 0.708 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".