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Record W3081795973 · doi:10.1108/jpbafm-05-2020-0070

Stretching the public purse: budgetary responses to COVID-19 in Canada

2020· article· en· W3081795973 on OpenAlexaffabout
Charles H. Cho, John Kurpierz

Bibliographic record

VenueJournal of Public Budgeting Accounting & Financial Management · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsYork University
Fundersnot available
KeywordsTransparency (behavior)PandemicDiversity (politics)OriginalityResilience (materials science)Coronavirus disease 2019 (COVID-19)Psychological resilienceValue (mathematics)Political sciencePoliticsEmergency responsePublic economicsPaymentBusinessEconomic growthEconomicsFinanceMedicineMedical emergency

Abstract

fetched live from OpenAlex

Purpose This paper summarizes the emergency measures taken by Canada in response to the COVID-19 pandemic, and discusses the key political, economic, and social factors that influenced the design of these measures. Design/methodology/approach This paper collects the announcement of emergency measures in the Canadian provincial and federal governments between March 18 and May 30, 2020 in response to the COVID-19 pandemic and categorizes them by type of emergency response. Findings Canada has a diversified response of emergency measures mediated by its various provinces. This suggests that Canada may be more robust to biological and economic threats than nations that have less policy diversity. Originality/value Canada's diversity of emergency measures allows for several different avenues for future research, including countercyclical spending by subnational polities, organizational diversity's effect on resilience, the effect of tax breaks versus direct or indirect payments, effectiveness of public-private partnerships, and the effect of transparency on citizen satisfaction.

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)0.005
metaresearch head score (Gemma)0.006
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 score0.643
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.050
GPT teacher head0.234
Teacher spread0.184 · 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

Citations23
Published2020
Admission routes2
Has abstractyes

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