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Record W2971720906 · doi:10.31857/s032120680006294-7

Canada’s Economic Situation before the 2019 Federal Election

2019· article· en· W2971720906 on OpenAlexaffabout
Lyudmila Nemova

Bibliographic record

VenueUSA & Canada Economics – Politics – Culture · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsInstitute for Christian Studies
Fundersnot available
KeywordsEarningsFederal budgetGovernment (linguistics)DebtInvestment (military)EconomicsEconomic policyPolitical scienceFinanceFiscal yearPoliticsLaw

Abstract

fetched live from OpenAlex

The article starts with a brief overview of the main economic trends and issues, which Canada has been facing since the end of 2015, when the Liberal party won the previous election and formed the majority government. That is followed by a detailed analysis of the key factors, which were shaping the dynamics of the socio-economic situation before the federal election scheduled for October 21, 2019. A weak growth of employment earnings and consumer spending, a high level of the household debt (about 100% of the GDP), a decreasing residential investment as well as a stagnant business investment have been the main factors slowing down Canada’s economic activity and exports growth since late 2018. At the same time, Canada’s economy proved to be highly vulnerable to disruptive external circumstances. The concluding part of the article looks at the recent manifestations and consequences of the most powerful sources of risks for the Canadian economy.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.172
Threshold uncertainty score0.961

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0140.002
Scholarly communication0.0080.001
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0090.001

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.

Opus teacher head0.005
GPT teacher head0.212
Teacher spread0.207 · 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 designNot applicable
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

Citations2
Published2019
Admission routes2
Has abstractyes

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