Labour Force Survey, May 2024 [Canada] [Rebased 2025]
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
The Labour Force Survey (LFS) provides estimates of employment and unemployment which are among the most timely and important measures of the performance of the Canadian economy. With the release of the survey results only 10 days after data are collected, the LFS is the first of Statistics Canada’s major monthly economic reports to be released. The methodology of the LFS is optimized to produce reliable information about month-to-month changes in key labour market indicators, such as the employment and unemployment rates.<br><br>Statistics Canada has an established history of applying a standard revision to its LFS estimates following the release of final population estimates from the most recent census. Data are revised to adopt the most recent geography, industry and occupation classifications; to take advantage of recent observations to fine-tune seasonal adjustment factors; or to introduce methodological enhancements. These revisions ensure that survey estimates accurately reflect the Canadian labour market, while having minimal impact on the comparability of labour market indicators, such as employment, unemployment, and participation rates over time. Updates made as part of this revision maintain coherence in the estimates of month-to-month and year-over-year changes.<br><br>The purpose of this document is to explain each of the revisions implemented in January 2025. It should be noted that these changes do not involve modifications to the questionnaire nor to the survey content. The following is a summary of each change:<ul><br><li><b>Population rebasing</b>: Until December 2024, the series of labour force estimates had been based on population control totals, derived from 2016 Census data (adjusted for net undercoverage). As of January 2025, the estimates have been adjusted to reflect population data from the 2021 Census and its coverage studies.</li><li><b>Geographical boundaries</b>: Census metropolitan areas (CMAs), economic regions (ERs), census agglomerations (CAs), and census subdivisions (CSDs) are now based on Standard Geographical Classification (SGC) 2021 - Volume I, The Classification. With this change, six new CMAs have been added (Fredericton, New Brunswick; Drummondville, Quebec; Red Deer, Alberta; Kamloops, Chilliwack, and Nanaimo, British Columbia). Boundaries for Employment Insurance Economic Regions (EIERs) remain unchanged. These new geographic areas are used starting from January 2011.</li><li><b>Gender</b>: To align with the departmental standard Classification of gender, LFS estimates now incorporate the concept of gender, which was introduced in the 2021 Census of Population and added to the LFS questionnaire in January 2022. As such, LFS data are based on sex of person up to December 2021 and gender of person from January 2022 onward. Although gender and sex at birth are two different concepts, this change does not cause a significant break in the trend because the two concepts produce very similar distributions. All data products from the LFS now adopt the term “gender” for all years and periods.</li><li><b>Industry and occupation classification update</b>: The LFS now uses the North American Industry Classification System (NAICS) Canada 2022 Version 1.0. Improvements were also made to the historical coding of occupation using National Occupational Classification (NOC) 2021 Version 1.0. These changes have minimal impact on estimates in published data tables and are primarily observed in the microdata and some custom tabulations. Revisions were extended back to 1987 for industry and 1998 for occupation.</li><li>Updates to landing month variable for immigrants.</li></ul>
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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