What has been done and what has not – the government of Canada’s economic policy response to the coronavirus pandemic
Bibliographic record
Abstract
Purpose This paper aims to examine Canadian government measures to support country’s economic recovery and sustainable development. The goal is to examine whether all orders of government are working well to deliver the required help to Canadians. Design/methodology/approach The theoretical foundations for this article are drawn from liberal and institutionalist approaches to comparative politics. Specifically, the proposed study draws on political tensions that occur because of actions of self-centered regional (provincial) governments who legitimize individual policies based on their self-centered economic and political objectives. Findings Nowadays, we can observe the primary role of the state in supporting and regulating the health governance systems, the economy and social life. Many informal groups have unstructured approach, which does not require them to follow existing strategies. The challenges caused by COVID-19 have led to the resurgence of collective, state-based approaches to the recovery. The key findings illuminate the importance of crisis communication activities which should be implemented properly. This implies that all disclosures must be timely and truthful. Practical implications The study helps to better understand the events that disrupt parts of the Canadian economy during pandemic. It reviews the essential functions that are critical for reliable operation of infrastructure services to ensure safety and well-being of the population. During the COVID-19, federal–provincial–territorial collaboration runs into resistance because of competing interests, resource constraints, legacies from past conflicts and lack of coordination. In contrast to managers, who often focus on tangible short-term results, today’s leadership more often seeks intangible long-term results. This means that the central–local government relations tend to be more informal. Originality/value In the face of external shock, such as COVID-19, it did not take much time for Canadian provincial governments to realize that they cannot cope with a wide range of challenges alone. In these circumstances, the narratives of how governments work together during the challenging time to impact their desired outcomes are of crucial importance.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.005 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".