Bibliographic record
Abstract
The article analyses the dynamics of the Canadian economy in 2020-2021, during the unprecedented global “pandemic” recession. It is shown that like in many other countries, the economic ups and downs in Canada closely followed the waves of the COVID-19 infection spreading across the regions and the subsequent rounds of regulatory restrictions on “high-contact” economic activities, citizens’ travel inside and outside the country, international trade, and etc. In the latter half of the 2020 several goods-producing industries showed signs of recovery which continued through the following year. However, it was only mass vaccination of Canadians in all provinces and territories that created conditions for sustained re-opening of businesses in most sectors of the national economy by the end 2021. The author looks at the internal and external drivers of recovery and continued growth. It is shown that on the whole the federal emergency plan proved to be successful in providing income support for Canadians and preventing bankruptcies among small and medium-sized businesses. The 2021 Federal Budget includes more than $100 billion in new spending over three years. It is expected that massive fiscal stimulus coupled with pent-up demand will sustain strong consumer spending after the speedy vaccine rollout allows businesses to fully reopen. At the same time, non-residential capital expenditures by private sector companies will increase only moderately in most sectors after a sizable decline in 2020. This year Canada’s resource-based industries are benefiting from the growing global demand for oil and gas, base metals, forest and agricultural products. The concluding part of the article analyses the major risks which can slow the economic recovery: the global supply-chain bottlenecks, labour market imbalances, growing inflation pressures, and massive federal budget deficit.
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 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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".