Revenue, Expenditure, Assets, and Liabilities (REAL) user files
Bibliographic record
Abstract
The Revenue, Expenditures, Assets, and Liabilities (REAL) data reports the fiscal statistics of all orders of government by province, for the years 1966 to the present. These datasets are the Finances of the Nation user files, and are designed for use by everyone. The data are offered in a "wide" format by revenue/expenditure item, making them easy to read and interpret. Four normalizations are offered of the data to enhance usability: Nominal dollars Percent of GDP Percent of total revenue Real per capita dollars Data sources The data sources are: 1965/66-1987/88: Public Finance Historical Data, Statistics Canada Catalogue 68-512 (1992) 1988/89-2007/08: Federal, provincial and territorial general government revenue and expenditures, Tables 10-10-0039 and -0040 2008/09-2018/19: Canadian government finance statistics, 2009-2019 (Tables 10-10-0016 and -0017) Methods Certain data from government public accounts, population and GDP, have also been used to render categories consistent through time. Future releases Future releases of the REAL data will include consolidated revenues for general and provincial-local governments, as well as assets and liabilities of governments. For 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.074 | 0.091 |
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".