Personal Income Statistics by top income share, in inflation adjusted dollars, User File
Bibliographic record
Abstract
Introduction The Personal Income Statistics present data on the distribution of income, deductions and tax credits by line item in the income tax return for the years 1992-2017. To ensure comparability over time, a consistent set of percentiles (vingtiles) are imputed for each year using a linear interpolation technique. This dataset This dataset shows the top income share in inflation adjusted dollars (2019 dollars). This dataset is a Finances of the Nation user file, and is designed for use by everyone. The data are offered in a "wide" by percentile form, with separate sheets for each year, making them easy to read and interpret. Separate files are included for each province, and for all provinces as a whole. Data sources The data sources are: T1 Final Statistics of the Canada Revenue Agency (CRA). Methods CRA's nominal income ranges are converted to percentiles by linear interpolation on the cumulative distribution function of each line item. Certain line items have been aggregated to render them comparable over time. Future releases This dataset will be updated as new years of T1 Final Statistics are published. A user guide is forthcoming. More information Please refer to www.financesofthenation.ca
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.015 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.081 | 0.067 |
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".