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Record W1981540370 · doi:10.3138/cpp.33.3.337

The Reform of Equalization Payments

2007· article· en· W1981540370 on OpenAlexaffvenueabout
Dan Usher

Bibliographic record

VenueCanadian Public Policy · 2007
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicTaxation and Legal Issues
Canadian institutionsQueen's University
Fundersnot available
KeywordsEntitlement (fair division)Transfer paymentPaymentRevenuePublic economicsEqualization (audio)Government (linguistics)EconomicsIncome taxBusinessFinanceWelfareMarket economyMicroeconomics

Abstract

fetched live from OpenAlex

A reasonable and fair interpretation of the mandate for equalization payments in Section 36(2) of the Canadian Constitution would differ from the present equalization formula in these respects: (a) transfers to the poorer provinces would be financed by transfers from the richer provinces rather than from the federal government; (b) entitlement to equalization payments would depend on provincial income rather than on a tax-by-tax comparison of the provinces' many tax bases; (c) for this comparison, provincial income would include revenue accruing directly to the provincial governments as well as the private income of residents of the province; and (d) compensation would be made for the exemption of provincial resource revenue from federal income tax. The most pronounced effect of these proposals would be to transfer the greater burden of equalization payments from Ontario to Alberta which is now, by far, the richest province.

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)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.948
Threshold uncertainty score0.517

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0090.011
Scholarly communication0.0060.003
Open science0.0020.003
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0060.001

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 head0.024
GPT teacher head0.264
Teacher spread0.240 · 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

Citations6
Published2007
Admission routes3
Has abstractyes

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