Income Trends in Canada, 1976-2006 [Canada] [B2020]
Bibliographic record
Abstract
Income Trends in Canada, is an extensive collection of income statistics, covering topics such as income distributions, income tax, government transfers, and low income. The data are drawn from two household surveys: the Survey of Consumer Finances (SCF) and the Survey of Labour and Income Dynamics (SLID). Historical data prior to 1996 are drawn from the SCF and data since 1996 are taken from SLID. In addition to provincial detail, many of the tables present estimates for the 15 largest Census Metropolitan Areas (CMAs), as follows: Halifax, Quebec, Montreal, Ottawa-Hull, Toronto, St.-Catharines - Niagara, Hamilton-Burlington, Kitchener-Waterloo, London, Windsor, Winnipeg, Calgary, Edmonton, Vancouver, Victoria. Due to the sample size limitations and sampling variability, estimates for urban areas are less reliable and are subject to larger errors than provincial and national estimates. Given the variability of the annual estimates, users are cautioned against drawing conclusions from single year-to-year comparisons alone.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.028 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.009 |
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".