A historical dataset of federal government spending changes for Canada
Bibliographic record
Abstract
This dataset presents a narrative record of all announced federal government spending changes in Canada between 1949q1 and 2012q1. We use the federal government's budget documents, mostly the budget speech, to document announced spending measures. Other budget documents that we use include the Economic Statements, Financial Statements, Mini Budgets, Interim Budgets, and Economic and Budget Updates. We document the motivation behind each announced measure. Based on these motivations, we classify spending changes as exogenous or endogenous. Exogenous changes are those that are not motivated by contemporaneous economic conditions of the country. Endogenous changes are those that are taken in response to current economic conditions. We also document the size of a change, whether it was intended to be temporary or permanent, the duration of the measure, and the size of the measure that was to be implemented in the same year as it is announced. This is the first dataset for any country that comprehensively presents all spending changes undertaken by a government.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".