Bibliographic record
Abstract
In the mid‐1990s Canada's federal government, concerned about a debt‐to‐GDP ratio that was approaching 70%, began a decade‐long policy of cutting government spending. It also increased taxes, but by only one dollar for about every six dollars of spending cuts. The Canadian government cut subsidies to individuals, corporations, and provincial governments while tightening eligibility for unemployment insurance. The government also sold off its holdings of various state‐owned enterprises. One major success was its shifting of air traffic control to NAV Canada, a private, non‐profit user cooperative. This step netted the government $1.4 billion at the outset, saved about $200 million a year in subsidies, and resulted in a technological revolution in air traffic control that has put Canada years ahead of the United States. From 1997 to 2008, Canada's government had an unbroken string of annual budget surpluses; and by 2009, Canada's debt‐to‐GDP ratio had fallen below 30%. Starting in 2000, the government used some of what otherwise would have been surplus to cut taxes on individuals and corporations. The corporate tax rate was cut in stages from 28% in 2000 to 21% by 2004.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.014 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.029 | 0.003 |
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".