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Record W3122486332

Numbers You can Trust? The Fiscal Accountability of Canada’s Senior Governments, 2017

2017· article· en· W3122486332 on OpenAlexaboutno aff
William B. P. Robson, Colin Busby

Bibliographic record

VenueC.D. Howe Institute Commentary · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsAccountabilityTransparency (behavior)RevenueTaxpayerGovernment (linguistics)AuditTreasuryLegislatureBusinessFinancePublic administrationAccountingPolitical science
DOInot available

Abstract

fetched live from OpenAlex

Canadians entrust their governments with major responsibilities, and pay taxes and underwrite borrowing to fund them. Financial reports are a vital tool for understanding governments’ financial plans and activities. Over the past 15 years, federal, provincial and territorial governments have done much to improve the transparency of their reports, and most of them have come closer to delivering on their budget promises. Yet too many continue to present opaque numbers, fail to satisfy their legislative auditors, take too long to present budgets or report, and spend far more than they budgeted. This latest edition of the C.D. Howe Institute’s annual report on the fiscal accountability of Canada’s senior governments assesses the quality of their financial information, and their success or failure in achieving their budgetary goals over the past 15 years. In looking at the quality of governments’ financial reporting, it asks whether an intelligent and motivated non-expert – a citizen, taxpayer or legislator – can get valid, timely and readily understood figures for total revenue and spending in the budget each government presents at the beginning of the year, and in the financial statements released with its public accounts at the end of the year. Alberta and New Brunswick earn the top scores for the quality and timeliness of their budgets and public accounts, with the federal government and British Columbia also doing well. Newfoundland and Labrador and Nova Scotia, though not in the top tier, have improved markedly. Quebec and Prince Edward Island do relatively poorly among the provinces, and Northwest Territories and Nunavut also present figures that our idealized reader would struggle to find and interpret. On the question of accuracy in hitting budget targets, the overall record is one of significant overshoots of both spending and revenue. On average, over all governments and all 15 years, governments spent 2.3 percent more than budgeted, which cumulates to a remarkable $69 billion. The Prairie provinces and the territories recorded the worst overruns; Ontario and Quebec did much better. Over the same period, revenues also overshot projections by 2.3 percent annually, cumulating to $95 billion more than budgeted. Although the overshoots tended to get smaller over the 15 years, a suspicious pattern of in-year spending “surprises” coinciding with in-year revenue “surprises” suggests less than prudent management of public funds. Legislators and Canadians generally should push senior governments to produce timelier, more transparent and reliable financial information, and should use that information to hold governments to account – at budget time, and throughout the fiscal year.

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)0.007
metaresearch head score (Gemma)0.030
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: Commentary
Teacher disagreement score0.838
Threshold uncertainty score0.972

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.030
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.005
Science and technology studies0.0180.005
Scholarly communication0.0170.005
Open science0.0020.004
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0170.003

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 head0.027
GPT teacher head0.298
Teacher spread0.270 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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