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Record W251931810

Managing Government Property Assets: International Experiences

2007· article· en· W251931810 on OpenAlexaboutno aff
Elaine Worzala

Bibliographic record

VenueJournal of Real Estate Literature · 2007
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing Market and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsReal estateGovernment (linguistics)Context (archaeology)Asset (computer security)Balance sheetProperty managementAsset managementPublic sectorDebtEconomicsPublic administrationFinanceBusinessEconomyPolitical scienceGeography
DOInot available

Abstract

fetched live from OpenAlex

Managing Government Property Assets: International Experiences. Olga Kaganova and James McKellar, Editors. 438 pages. The Urban Institute Press, Washington, D.C., 2006 Managing Government Property Assets: International Experiences is a compilation of the work of seventeen authors who have focused their careers on helping central, regional, and local governments become better owners, managers, and users of their real estate assets. It is edited by two prominent researchers in the area of public sector property-Olga Kaganova from the Urban Institute and James McLellar from York University in Toronto, Canada. The international context of the book provides the reader with a very rich, comparative approach that examines both the practices found in developed economies, as well as the more recent practices being developed in the emerging economies. In some cases, the latter group is more advanced because they have had the opportunity and expertise provided by some of the authors of this book to establish new systems and they are not ''bogged down'' in inherited systems passed down for generations. The early chapters outline common problems associated with the management of government-owned property assets. The cases of Canada, Australia, New Zealand, and France are explored. All are countries in which central governments have reformed property asset management as part of an overall effort to improve public sector efficiency and address issues of sustained fiscal deficits and growing public debt. At the municipal level, the book offers a strategic view of the municipal balance sheet and draws upon asset management experiences in the United States, Switzerland, and Germany. Authors lay out the lessons that have been learned from municipal asset management reform in countries with emerging markets. The book then focuses on special instruments and information systems that are now being tried in some countries, including special purpose corporations and public-private partnerships (PPPs). In conclusion, the book sets out a reform framework for countries and cities that want to start reforms and suggests topics for further research and examination. The compilation of essays results in some interesting findings: * First, prior to the reforms introduced by governments such as Canada, Australia, and New Zealand, management of real property assets has been badly handled in all types of economies. * Asset management by local governments varies widely even within one country, and often is either absent or embryonic. * Even in a regime of direct democracy, such as Switzerland, citizens have the illusion that when democratic rules are respected, efficiency follows. Within the Roman-law systems, the emphasis is on the ownership of material assets, not their maintenance, management, or potential for delivering economic benefits. Most likely, this focus on ownership per se is implicitly adhered to in many countries and trumps all other considerations, including effective asset management. This unspoken principle appears to guide politicians, who often find that voters are more concerned with retaining a public asset than utilizing it wisely. …

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)0.003
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: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.004
Science and technology studies0.0060.005
Scholarly communication0.0100.015
Open science0.0010.008
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0090.002

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 head0.013
GPT teacher head0.223
Teacher spread0.210 · 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
GenreReview

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
Published2007
Admission routes1
Has abstractyes

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