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Record W3047674918 · doi:10.14738/abr.87.8770

The Impact of COVID-19 on the Canadian Economy

2020· article· en· W3047674918 on OpenAlexaffabout
Sadequl Islam, Tahsina Tarannum

Bibliographic record

VenueArchives of Business Research · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCOVID-19 Pandemic Impacts
Canadian institutionsUniversity of TorontoLaurentian University
Fundersnot available
KeywordsIndex (typography)RecreationCoronavirus disease 2019 (COVID-19)TourismUnemploymentEconomicsAccommodationLabour economicsPandemicDemographic economicsEntertainmentUnemployment rateBusinessEconomic growthGeography

Abstract

fetched live from OpenAlex

This paper examines various types of effects of COVID-19 on Canadian businesses and industrial sectors. Using the data from Labour Force Surveys, the paper explores the impact of COVID-19 on the Canadian labour market. The paper also investigates the relationship between the intensity of lockdown measures ( stringency index) and the unemployment rate for Canada and selected countries. The main findings are the following: 1)The businesses which faced a high level of decreases in demand are food &accommodation, arts, entertainment, and recreation and retail trade; 2) Small businesses witnessed a higher level of decrease in demand compared to large businesses; 3) The goods- producing industries, especially motor vehicle and parts producing industries experienced the steepest decline in growth rates; 4)The pandemic adversely affected the jobs of women, workers with high school education, and young workers; 5) Finally, it appears that there is a weak positive relationship between the Stringency Index and the unemployment rate across selected countries including Canada.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.079
Threshold uncertainty score0.572

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0040.002
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.193
GPT teacher head0.363
Teacher spread0.170 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2020
Admission routes2
Has abstractyes

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