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Record W7020540626

Managing the infrastructure gap: perceptions, challenges and strategies in Calgary and Edmonton

2022· dissertation· en· W7020540626 on OpenAlexaboutno aff

Bibliographic record

VenueUWSpace (University of Waterloo) · 2022
Typedissertation
Languageen
FieldSocial Sciences
TopicUrban Planning and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsAsset managementAsset (computer security)PoliticsUrban infrastructurePublic infrastructureCapital (architecture)Work (physics)Descriptive statisticsLocal government
DOInot available

Abstract

fetched live from OpenAlex

Since the 1980’s, concerns over the municipal ‘infrastructure gap’ or ‘deficit’ have been frequently raised by local governments in Canada and around the world. In response to the limited number of in-depth case studies on this topic, this thesis undertook a comparative analysis of the Cities of Calgary and Edmonton focusing on how these two municipalities understand, view the causes of, and are working to address, their local infrastructure gaps. Municipal councillors and administrators, as well as developer and community representatives, were interviewed to discuss their views on their respective city’s infrastructure gap. Document analysis and descriptive statistics were employed to triangulate findings. This thesis finds that both cities treat their infrastructure gap figures as high-level estimates subject to significant uncertainty and produced, at least in part, to assist with advocacy for increased intergovernmental capital transfers. Participants in both cities identified the following as contributing to their infrastructure gaps: growth and urban form, asset management challenges, political factors, and inadequate revenues. Participants frequently described infrastructure, particularly maintenance and renewal, as being “not sexy” politically and therefore difficult to adequately prioritize. Both cities exclusively report funding gaps for tax-supported (e.g., roads, public transit) infrastructure; neither city reports funding gaps for user fee-funded infrastructure (e.g., water utilities, waste and recycling). Despite this, no participant identified (the absence of) pricing as contributing to infrastructure gaps. This thesis also finds that both case cities have, particularly in the last two decades, exerted substantial effort to improve their capital budgeting and asset management processes to optimize limited funding. Both cities’ strategies correspond with understood causes of infrastructure gaps and include improved asset and growth management practices (particularly related to cost modeling), development of capital prioritization methods, cultivation of dedicated revenue streams (including earmarked property tax increases and development charges), and advocacy for increased intergovernmental transfers. \nIn response to the above, the following recommendations are made: a national framework should be developed to monitor municipal service levels and associated service costs; municipalities should continue to improve their understanding of capital costs associated with urban growth and proper asset management; municipalities should improve the transparency of their capital budgeting and prioritization decisions; provinces should provide large, urban municipalities with additional own-source revenue tools to better respond to their infrastructure needs; and municipalities should seriously assess the feasibility of introducing pricing (e.g. road tolling) to fund needed infrastructure.

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)0.004
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.145
Threshold uncertainty score0.447

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0220.012
Scholarly communication0.0110.003
Open science0.0030.012
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.012
GPT teacher head0.228
Teacher spread0.216 · 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 designQualitative
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
Published2022
Admission routes1
Has abstractyes

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