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Record W2261620324 · doi:10.55016/ojs/sppp.v6i1.42422

A Recovery Program for Alberta: A 10-Year Plan to End the Addiction to Resource Revenues

2013· article· en· W2261620324 on OpenAlexaffabout
Ronald D. Kneebone, Margarita Gres

Bibliographic record

VenueThe School of Public Policy Publications · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsRevenuePlan (archaeology)Resource (disambiguation)BusinessAddictionOperations managementFinanceComputer scienceEconomicsGeographyPsychologyPsychiatryArchaeology

Abstract

fetched live from OpenAlex

Alberta has a substance-abuse problem. The substance is fossil fuels, and the province has become hooked on the revenues from oil and gas sales to fund its spending on health, education and social services. As we are so often told, the first step in beating an addiction is admitting that a compulsion has gotten out of control. Recent announcements suggest that Alberta’s leaders appear to have finally taken that first crucial step. We applaud them for doing so. But if they plan to get this addiction under control and so ward off the sort of financial turmoil that has tormented Alberta in the past, they will have to do more. In this note we provide a menu of policy choices all of which take the government to a sustainable budget by 2023. They all involve reductions in what we identify as the government’s Budget Gap — that is, the difference between its spending and all its revenue besides the revenue it earns from nonrenewable resources. The size of that gap summarizes just how much provincial government spending on health care, education and social services is at the mercy of commodity-market swings. If current trajectories of government spending continue, then in another 10 years the gap will be nearly 4 times what it was in 1999. Reducing the size of the Budget Gap is necessary to protect Albertans from repeatedly suffering wide swings in levels of public service, shifting tax rates and plunges into deficit and debt. We identify a variety of ways to achieve fiscal sustainability over 10 years. Our investigation highlights two key results. First, provincial spending on health care currently comprises 40 per cent of provincial expenditures and is growing at a rate that causes it to double every 20 years. Exempting health care spending from cuts comes at the price of draconian cuts to education and social services of over 30% even after adjusting for inflation and population growth. It is therefore hard to fathom that constraints on health spending can be avoided altogether. Second, to reduce the size of the cuts to spending required to achieve fiscal sustainability, the government can raise rates on existing taxes or introduce a new source of revenue like a sales tax. It is important to note, however, that new revenue without spending restraint cannot solve the problem. Additional revenue can only help achieve fiscal sustainability if it is accompanied by a program of spending restraint along with a sales tax of 3, 6 and 9 per cent and an increase in the personal tax rate to between 12 and 17 per cent from its current 10 per cent. None of these are easy options. But weaning itself off of its addiction to resource revenue means Alberta’s days of taking the easy way are over. Spending cuts alone or spending cuts in conjunction with increases in taxes are necessary steps to recovery. The government of Alberta has finally admitted it has a problem. In this note we identify the ways it can fix it.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.987
Threshold uncertainty score0.792

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0060.002
Scholarly communication0.0040.001
Open science0.0030.006
Research integrity0.0070.006
Insufficient payload (model declined to judge)0.0260.004

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 head0.034
GPT teacher head0.320
Teacher spread0.286 · 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 designTheoretical or conceptual
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

Citations0
Published2013
Admission routes2
Has abstractyes

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