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Record W3125711827

Challenges, Growth and Opportunity: A Shadow Federal Budget for 2015

2015· article· en· W3125711827 on OpenAlexaboutno aff
William B. P. Robson, Alexandre Laurin

Bibliographic record

VenueC.D. Howe Institute Commentary · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsFinancePrudenceFederal budgetBusinessEconomicsRevenueTransparency (behavior)Economic policyFiscal year
DOInot available

Abstract

fetched live from OpenAlex

The 2015 edition of the C.D. Howe Institute’s annual Shadow Federal Budget lays out a prudent fiscal course in the face of a challenging world economic environment, bolstering the confidence of Canadians in the sustainability of their public finances. It supports economic growth with tax changes and investments that support business investment and job creation. And it enhances opportunities for Canadians by investing in their skills for the workplace and better preparing them for retirement. The first order of business for this Shadow Budget is to prevent a slide back into red ink. Recent world commodity market swings makes near-term economic growth and fiscal revenue forecasts more uncertain. Doubling the amounts set aside for prudence, selling the government’s remaining interest in airport leases, and containing the cost of federal government operations, will protect the budget surplus and strengthen the balance sheet. Measures to improve the transparency of federal finances through a restatement of Ottawa’s obligations for employee pensions, and supplementary information on the present value of future taxes on tax-deferred savings, will facilitate the adoption of more forward-looking fiscal policies. Strong and sustainable economic growth requires policies to promote adjustment to changing market conditions and deploy Canada’s physical and technological resources where they will do the most good. Measures to boost Canada’s economic dynamism include creating a more efficient payments system, reviewing capital consumption allowances, and ensuring that Crown financial corporations have a clearly articulated mandate complementary to private lending institutions. Fresh infrastructure spending – $1.7 billion – is devoted to national air and rail transportation networks, two areas where federal involvement is uniquely appropriate, and where important economic benefits can be reaped. On the tax side, the introduction of an allowance for corporate equity investments (on a revenue neutral basis) in the computation of corporate income taxes will improve the environment for business investment and reduce distortions toward debt-financed investments. As for innovation and technology adoption, the introduction, as several countries already have, of a “patent box” will encourage the commercialization and use of innovative ideas developed in Canada. Canadians’ economic prosperity depends in large part on the opportunities they have to develop their knowledge and skills, to deploy them during their work lives, and to save and draw down savings once their work lives are over. This Shadow Budget takes several steps to improve and protect these opportunities, including boosting funding for benchmarking the performance of education systems, expanding eligibility for tax preferences on pension income and retirement savings, and eliminating mandatory minimum drawdowns from Registered Retirement Income Funds (RRIFs) and similar accounts. The plan for confronting the fiscal challenges ahead, promoting growth and ensuring better opportunities for Canadians will be cost-effective, leaving the budget in surplus.

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)0.008
metaresearch head score (Gemma)0.015
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: Commentary · Consensus signal: none
Teacher disagreement score0.604
Threshold uncertainty score0.788

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.015
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0060.001
Scholarly communication0.0120.005
Open science0.0030.007
Research integrity0.0080.010
Insufficient payload (model declined to judge)0.0450.019

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 head0.133
GPT teacher head0.343
Teacher spread0.211 · 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
GenreCommentary

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
Published2015
Admission routes1
Has abstractyes

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