Slaying the Dragon: Deficit Reduction in Canada and the United States, 1980-2000
Bibliographic record
Abstract
The purpose of this thesis is to analyze budget deficits in Canada and the United States from the time period of 1980 until 2000. This thesis will start out with an analysis of the literature surrounding budget deficits and surpluses and will provide a thumbnail sketch of what factors affect budgetary deficits and surpluses. We will then move on to an analysis of these theories examined through the lens of our two case studi es, Canada and the United States from 1980 until 2000. The thesis will end with an analysis of the policies employed in the United States and Canada in order to get their fiscal houses under control. By the end of this work, I hope that the reader will understand the factors that affected budget policymaking in Canada and the United States from 1980 to 2000. We will discover that Canada tended to pursue a cutback-based solution to budget balance, whereas the United States tended to pursue a revenue-based solution. We will also find that there were differing political factors that affected the outcomes in both nations.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".