MétaCan
Menu
Back to cohort
Record W4205405240 · doi:10.31857/s086904990017875-4

The COVID-19 Pandemic: Key Factors in Canada’s Social Policy Development

2021· article· en· W4205405240 on OpenAlexaboutno aff

Bibliographic record

VenueObshchestvennye nauki i sovremennost · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCOVID-19 Pandemic Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsPandemicGovernment (linguistics)Economic growthPoliticsPolitical scienceCoronavirus disease 2019 (COVID-19)EliteSocial policyRelevance (law)Social protectionPublic relationsDevelopment economicsEconomicsMedicineDiseaseLaw

Abstract

fetched live from OpenAlex

Many studies focus on the COVID-19 pandemic, response mechanisms and response design. At the same time, it becomes more and more obvious that not only the study of economic policy and decisions made by the government in connection with the pandemic is acquiring relevance. As the spread of the disease continues, social problems and difficulties that political elites will have to deal with are exposed in Canadian society. Despite the fact that the government has taken unprecedented measures to expand assistance and social protection to the most vulnerable groups – low-income families, women, senior citizens, young people and children, low-skilled workers, self-employed, people with disabilities, etc. – the pandemic, however, has had a profound impact on society. The government will be forced to adapt its policies in the field of social protection and labor relations, in the field of health and education. This article seeks to explore the key social dimensions of the COVID-19 pandemic, as well as factors that can influence the political decisions of the Canadian elite in the near future.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.638
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.117
GPT teacher head0.292
Teacher spread0.175 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueObshchestvennye nauki i sovremennostSame topicCOVID-19 Pandemic ImpactsFrench-language works237,207