Bibliographic record
Abstract
This year the world is faced with a new global challenge of novel coronavirus, a strategy for responding to which each country has developed its own. For many countries, the spread of COVID-19 has highlighted problems and exposed weaknesses that have become more pronounced every month. Canada is no exception. Despite the developed economy, strong democratic institutions and effective governance at the federal, provincial and territorial levels, the pandemic has become a threat not only to people’s lives, but also to the economic and political systems. The federal government, as well as provincial and territorial authorities were placed in unprecedented conditions when it was necessary to make tough decisions on the introduction of restrictive measures (including a ban on mass events, restrictions on freedom of movement, mandatory quarantine for visitors) and at the same time timely financial support for the Canadian population. The federal center, together with regional authorities – provinces and territories – have formed an integral system of response measures in various areas, including support mechanisms for individuals (students, elderly people, persons with disabilities, indigenous people, etc.), Canadian NGOs, business – community, various sectors of the economy – from agriculture to energy. Also a system of timely exchange of information and data, increasing the dynamism and efficiency of political decision-making process was launched. This article is about the multidimensional focus of Canada's response to the COVID-19 outbreak, plans for responding to the new challenge at the federal and provincial-territorial levels, and how politically effective the federal government's decisions have been.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.003 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".