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Record W3097433462 · doi:10.32721/ctj.2020.68.3.fon

Finances of the Nation: The Economic Response of Governments in Canada to COVID-19 in the First Three Months of the Crisis

2020· article· en· W3097433462 on OpenAlexaffvenueabout
Julie S. Gosselin, Luc Godbout, Tommy Gagné-Dubé, Suzie St‐Cerny

Bibliographic record

VenueCanadian Tax Journal/Revue fiscale canadienne · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Political scienceSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakPublic administrationEconomic growthEconomicsMedicine

Abstract

fetched live from OpenAlex

For almost 60 years, the Canadian Tax Foundation published an annual monograph, <i>Finances of the Nation</i>, and its predecessor, <i>The National Finances</i>. In a change of format, the 2014 <i>Canadian Tax Journal</i> introduced a new "Finances of the Nation" feature, which presents annual surveys of provincial and territorial budgets and topical articles on taxation and public expenditures in Canada. In this article, the authors recount how Canadian governments have responded to the COVID-19 crisis through the economic measures announced between March 11 and May 15, 2020. Their analysis shows that Canadian governments have acted quite rapidly and that, by expanding emergency measures, they have tried to help just about everyone. The authors set out the estimated costs of the various measures, followed by a brief comparison of Canada's response with the responses of other countries. The underlying data for the <i>Finances of the Nation</i> monographs and for the articles in this journal will be published online 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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.256
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.000
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 head0.034
GPT teacher head0.188
Teacher spread0.154 · 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.

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

Citations3
Published2020
Admission routes3
Has abstractyes

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