Producing official statistics via voluntary surveys -- the National Household Survey in Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Statistics Canada conducts over 350 business, social and institutional surveys a year. Of all social or household type surveys, only one in addition to the Census of Population is conducted on a mandatory basis, the Labour Force Survey. By their very nature, voluntary surveys will achieve lower rates of response and are thus exposed to higher risks of bias. For the 2011 Census of Population program, the long form census was for the first time collected on a voluntary basis as the National Household Survey. The survey content was basically the same as that of previous Census long forms and covered various socio-demographic topics that are of high importance to a wide variety of stakeholders in Canada. Given that one of the key characteristics of a census is to produce data for small regions and for subgroups of the population, collecting the survey on a voluntary basis introduced several challenges. Statistics Canada, based on its extensive experience with voluntary surveys, developed a number of processes and approaches to ensure the highest data quality possible. This paper describes what these measures were for data collection, data processing and estimation. It also provides a brief description of the quality assurance processes underlying the release strategy of the 2011 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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it