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50 Years of Government of Alberta Budgeting

2018· article· en· W6884658295 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Calgary · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsRevenueVolatility (finance)Government (linguistics)Government revenueGovernment spendingFiscal policy

Abstract

fetched live from OpenAlex

This briefing note uses a newly completed time series on the government of Alberta’s finances to present a broad overview of the government’s budgetary choices since fiscal year 1965–66. The note paints a picture using broad strokes that focuses on the government’s attempts to deal with volatile energy revenues. It shows that over the past 50 years the government has made a policy choice to allow volatility in energy revenues to create volatility in its budget. This policy choice has resulted in occasional bouts of severe spending contractions and likely encouraged higher rates of spending and lower taxation than would otherwise have been observed. These outcomes are the result of the government failing to heed the advice of economists, namely, to save energy revenues and in this way establish a steady and reliable source of revenue. In the note we describe a number of strategies the government has used over the years to reduce its reliance on energy revenue. Success came only after a dramatic cut to program spending in the mid-1990s. Only during this brief period in the mid-1990s was the government able to fund current expenditures without the need for energy revenues. To use a phrase made popular in the 2015 provincial election campaign, for that brief period in the mid-1990s, the government had managed to climb “off the energy roller-coaster.” But it could not stay off, and the government, with the support of voters, returned to a pattern of financing spending growth not with taxation but with energy revenues. At the time of writing this note, the current government is suffering the consequences of a budget based on spending and tax choices that require a heavy reliance on energy revenues to find balance. Getting off the energy roller coaster requires new revenue, cuts to program spending, or some combination of the two. To remain off the roller coaster requires a commitment of the sort previous governments have been unable to stick to.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.794
Threshold uncertainty score0.886

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.209
Teacher spread0.201 · 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 teacher head, 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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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