Canadian Government Debt and Deficit Spending in a Time of Crisis
Bibliographic record
Abstract
Since its identification in December of 2019, every nation on this planet has been faced with a once-in-a-century battle against the coronavirus disease, COVID-19. As countries grappled with their responses to the pandemic, the Canadian government spent an unprecedented amount of money to provide support to businesses that were forced to close due to lockdowns, and consequently, citizens who lost their jobs. As a result of these emergency measures and similar ones enacted in other countries, 2020 saw the largest global economic downturn since the Great Depression. Government debt and deficit spending in the Canadian context is increasingly becoming a contentious political issue which warrants an extensive review of literature and past policies to map a path forward. This paper will analyze austerity and Keynesianism, two political-economic policy strategies to address the growing government debt resulting from COVID-19. Given the failures of austerity policies in alleviating economic downturns in recent crises, this paper will argue that the best strategy to address post-COVID government debt is to enact Keynesian stimulatory fiscal policy to produce economic growth. Such a strategy would provide the best economic outcome and avoid the pitfalls of austerity, which often reduces the well-being of society by cutting social programs and promoting class and gendered inequality. The pandemic has exposed shortcomings in the current economic and welfare systems reinforced by neoliberal austerity. These shortcomings have not only been exacerbated by the pandemic, but also risk hindering a more efficient and equitable recovery.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".