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Record W2974989533

Finances of the Nation: "Final and Unalterable"--But Up for Negotiation: Federal-Provincial Transfers in Canada

2018· article· en· W2974989533 on OpenAlexaffabout
Trevor Tombe

Bibliographic record

VenueSSRN Electronic Journal · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsNegotiationProduct (mathematics)Political scienceGross domestic productPublic administrationEconomicsLawEconomic growth
DOInot available

Abstract

fetched live from OpenAlex

For almost 60 years, the Canadian Tax Foundation published an annual monograph, of the Nation, and its predecessor, The National Finances. In a change of format, the 2014 Canadian Tax Journal introduced a new Finances of the feature, which presents annual surveys of provincial and territorial budgets, and topical articles on taxation and public expenditures in Canada. In this article, Trevor Tombe explores the history of federal-provincial transfers in Canada. He compiles and analyzes uniquely detailed data from Confederation to the present showing that (1) explicit transfers to provincial governments are more equally distributed today than they have been throughout most of Canada's history, and (2) while overall federal tax and spending activities currently redistribute just under 2 percent of Canada's gross domestic product across provinces, this is the lowest level in the past six decades. Tombe proposes a uniform methodology to quantify and analyze explicit and implicit fiscal transfers, discusses the design of today's transfer programs and the pressures that they must withstand, and suggests some changes that might be considered in future reforms. The underlying data for the of the Nation monographs and 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 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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.783
Threshold uncertainty score0.908

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.008
Science and technology studies0.0170.006
Scholarly communication0.0090.003
Open science0.0010.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.015
GPT teacher head0.250
Teacher spread0.235 · 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

Citations1
Published2018
Admission routes2
Has abstractyes

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