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Record W1970647462 · doi:10.1108/14725960510808383

Coming of age: Strategic asset management in the municipal sector

2004· article· en· W1970647462 on OpenAlexaff
Pierre W. Jolicoeur, James T. Barrett

Bibliographic record

VenueJournal of Facilities Management · 2004
Typearticle
Languageen
FieldPsychology
TopicFacilities and Workplace Management
Canadian institutionsShared Services Canada
Fundersnot available
KeywordsRationalisationAsset managementBusinessLocal governmentAsset (computer security)Property managementService (business)Public sectorGovernment (linguistics)Service delivery frameworkStrategic managementIT asset managementFinanceOperations managementMarketingEconomicsPublic administrationEconomyComputer scienceReal estate

Abstract

fetched live from OpenAlex

The application of strategic asset management in the municipal sector is of growing concern and importance. Increasingly, municipalities are faced with shrinking facility budgets while, at the same time, having to provide the most suitable properties in support of core service delivery requirements. The focus of municipal asset management is to support local decision making related to the acquisition, remediation or disposal of property. In light of the fact that local government is the closest level of government to the public, the framework for strategic asset management must be transparent and beyond reproach. This paper legitimises the application of strategic asset management in the municipal sector and proffers a unique, empirical approach to the rationalisation of property in support of service delivery.

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.007
Scholarly communication0.0090.005
Open science0.0010.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.058
GPT teacher head0.306
Teacher spread0.248 · 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 designObservational
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

Citations43
Published2004
Admission routes1
Has abstractyes

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