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Record W3123987247

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

2018· article· en· W3123987247 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

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

fetched live from OpenAlex

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.

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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.160
Threshold uncertainty score0.670

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 studies0.0010.001
Scholarly communication0.0000.001
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 head0.031
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