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Record W3123987247

Wild Numbers: Getting Better Fiscal Accountability in Canada’s Municipalities

2018· article· en· W3123987247 on OpenAlexaboutno aff
William B. P. Robson, Farah Omran

Bibliographic record

VenueC.D. Howe Institute Commentary · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsTaxpayerAccountabilityCapital expenditureFiscal yearBusinessAccountingOperating budgetQuality (philosophy)FinancePublic economicsEconomicsPolitical science
DOInot available

Abstract

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Canada’s municipalities deliver services that are critical to quality of life, and require major commitments of resources in taxes, fees and intergovernmental transfers. But their budgeting practices, and people’s ability to measure their municipality’s performance against its budget commitments, are nowhere near the level appropriate to this importance. This report looks at the annual spending projections in the budgets of 31 of Canada’s largest municipalities since 2009, and the results reported in those municipalities’ financial statements at the end of each of those years. It asks what a councillor, or taxpayer, or citizen – a person who is motivated and numerate, but non-expert, would infer from each budget, and how close this same person would judge the municipality had come to its spending targets when inspecting the municipality’s reported expenses. In most municipalities, simply finding numbers that describe spending plans in budgets is a challenge: very few budget documents even contain numbers on the same accounting basis used in the financial statements. Users who do put the time and effort into finding numbers describing their municipality’s operating and capital spending plans, and compare them to the expenses reported after year end, would typically conclude that the municipality did a terrible job of hitting its budget projections. Over the past nine years this study looks at – from 2009, when Canada’s cities began reporting their results using Public Sector Accounting Standards (PSAS), to 2017, the most recent year available – these 31 cities have typically undershot those projections on average over that period; and missed them in one direction or another by an average of 9 percent. Improving this situation is partly a matter of presenting budgets using the same comprehensive PSAS-consistent revenue and expense numbers that municipalities already use in their financial statements. Provinces that mandate municipal budgets prepared in other ways – splitting operating and capital budgets, with the latter prepared on an antiquated cash basis – should stop doing so. Either way, municipalities can show PSAS-consistent numbers as supplementary information on their own, and can take other steps to ensure that their budgets represent the full picture of the municipality’s activities and its claim on citizens’ resources. Better matching of results with budget plans will also require councillors, ratepayers, and voters to demand – and get – timely budgets, regular updates in interim reports, and rapid publication of final results. Those are all key tools to help them compare budget plans to past results, and current results to past plans – and, when circumstances warrant, demand corrective action. Councillors, ratepayers, and voters should insist on better numbers from their municipalities, and on the improved fiscal accountability the better numbers will make possible.

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)0.016
metaresearch head score (Gemma)0.054
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 score0.168
Threshold uncertainty score0.965

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.054
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.007
Science and technology studies0.0330.008
Scholarly communication0.0210.009
Open science0.0030.010
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0110.002

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 head0.031
GPT teacher head0.298
Teacher spread0.268 · 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
Published2018
Admission routes1
Has abstractyes

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