Survey of Labour and Income Dynamics, 2001 [Canada] : Cross-Sectional Census Family File
Bibliographic record
Abstract
The cross-sectional public-use microdata file for the Survey of Labour and Income Dynamics (SLID) is a collection of income, labour and family variables on persons in Canada and their families. SLID is an annual household survey covering the population of the 10 Canadian provinces with the exception of Indian reserves, residents of institutions and military barracks. The Survey of Labour and Income Dynamics began collecting data for reference year 1993. Initially, SLID was designed to be, first and foremost, a longitudinal survey, with primary focus on labour and income and the relationships between them and family composition. Initially, two versions of SLID public-use microdata files were released: the first cross-sectional set covering reference year 1993 and the second longitudinal covering reference years 1993 and 1994. Both cross-sectional and longitudinal public-use files were released. After the release of the 1993 and 1994 files, the decision was made to extend the objectives of SLID to be the primary source of cross-sectional household income data. The type of income data collected by SLID was identical to that of the former household income survey SCF (Survey of Consumer Finances), with the distinction that SLID respondents had the choice of a traditional income interview and granting permission to Statistics Canada to use their T1 income tax data. For many years, the Survey of Consumer Finances had provided public-use microdata files ( PUMFs ) to meet the needs of cross-sectional household income data users. SCF PUMFs were released up to and including reference year 1997. For the purpose of standard publications, Statistics Canada has made the transition from SCF to SLID between 1995 and 1996. Therefore, SLID cross-sectional PUMFs are being made available beginning with reference year 1996. The SLID files have been designed to be analogous to those produced for the SCF.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.025 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.014 |
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".