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Record W4308968696 · doi:10.1108/oxan-db273987

Canada uses rising revenues for targeted spending

2022· article· en· W4308968696 on OpenAlexaboutno aff

Bibliographic record

VenueEmerald expert briefings · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsPrime ministerRevenueRepealGovernment (linguistics)Inflation (cosmology)EconomicsPublic spendingPublic sectorGovernment budgetGovernment spendingEconomic policyPublic economicsPolitical scienceWelfareFinanceMacroeconomicsPublic financeEconomyPoliticsMarket economyLaw

Abstract

fetched live from OpenAlex

Significance The government of Prime Minister Justin Trudeau has previously emphasised spending but, amid high inflation and consequent high interest rates, Freeland stressed the need for restraint, promising a balanced budget within four years. Even so, she has increased assistance to students and Canadians on low incomes who are struggling with the cost of living. Impacts The Conservatives will continue to criticise spending measures in Freeland’s statement but will not repeal them if they win power. The Bank of Canada is expected to raise its benchmark interest rate, currently 3.75%, by at least 50 basis points next month. Although the government is emphasising restraint, this is unlikely to translate into public sector job cuts in the next two years. If elected, a Conservative government would likely take a more stringent approach to the public sector, risking strike disruption.

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)0.003
metaresearch head score (Gemma)0.012
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: Other · Consensus signal: Other
Teacher disagreement score0.104
Threshold uncertainty score0.753

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0100.005
Scholarly communication0.0110.002
Open science0.0010.004
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0240.003

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 head0.027
GPT teacher head0.294
Teacher spread0.267 · 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
GenreOther

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
Published2022
Admission routes1
Has abstractyes

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