Canada budget benefits from higher government revenues
Bibliographic record
Abstract
Significance Promising to foster post-pandemic growth, the Liberal government is increasing spending on green technology and climate change mitigation, but the focus of the budget is on social spending. An unanticipated surge in government revenues in recent months makes this possible, despite earlier concerns about substantial deficits following the high levels of pandemic-related spending over the last two years. Impacts The new Canada Growth Fund, designed to address the country’s rising lack of competitiveness, will have little immediate impact. Despite a boost in the budget, low levels of defence spending will limit Canadian input as NATO responds to the war in Ukraine. Because making land available for development is a provincial issue, new federal funding for housing will have limited effect. The budget supports continued immigration, with Canada accepting 450,000 new immigrants per year by 2024.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.162 | 0.039 |
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".